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Davis, James W. "Gesture recognition." Honors in the Major Thesis, University of Central Florida, 1994. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/126.
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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.Bachelors
Arts and Sciences
Computer Science
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Cheng, You-Chi. "Robust gesture recognition." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53492.
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It is a challenging problem to make a general hand gesture recognition system work in a practical operation environment. In this study, it is mainly focused on recognizing English letters and digits performed near the steering wheel of a car and captured by a video camera. Like most human computer interaction (HCI) scenarios, the in-car gesture recognition suffers from various robustness issues, including multiple human factors and highly varying lighting conditions. It therefore brings up quite a few research issues to be addressed. First, multiple gesturing alternatives may share the same meaning, which is not typical in most previous systems. Next, gestures may not be the same as expected because users cannot see what exactly has been written, which increases the gesture diversity significantly.In addition, varying illumination conditions will make hand detection trivial and thus result in noisy hand gestures. And most severely, users will tend to perform letters at a fast pace, which may result in lack of frames for well-describing gestures. Since users are allowed to perform gestures in free-style, multiple alternatives and variations should be considered while modeling gestures. The main contribution of this work is to analyze and address these challenging issues step-by-step such that eventually the robustness of the whole system can be effectively improved. By choosing color-space representation and performing the compensation techniques for varying recording conditions, the hand detection performance for multiple illumination conditions is first enhanced. Furthermore, the issues of low frame rate and different gesturing tempo will be separately resolved via the cubic B-spline interpolation and i-vector method for feature extraction. Finally, remaining issues will be handled by other modeling techniques such as sub-letter stroke modeling. According to experimental results based on the above strategies, the proposed framework clearly improved the system robustness and thus encouraged the future research direction on exploring more discriminative features and modeling techniques.
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Kaâniche, Mohamed Bécha. "Human gesture recognition." Nice, 2009. http://www.theses.fr/2009NICE4032.
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Dans cette thèse, nous voulons reconnaître les gestes (par ex. Lever la main) et plus généralement les actions brèves (par ex. Tomber, se baisser) effectués par un individu. De nombreux travaux ont été proposés afin de reconnaître des gestes dans un contexte précis (par ex. En laboratoire) à l’aide d’une multiplicité de capteurs (par ex. Réseaux de cameras ou individu observé muni de marqueurs). Malgré ces hypothèses simplificatrices, la reconnaissance de gestes reste souvent ambiguë en fonction de la position de l’individu par rapport aux caméras. Nous proposons de réduire ces hypothèses afin de concevoir un algorithme général permettant de reconnaître des gestes d’un individu évoluant dans un environnement quelconque et observé `a l’aide d’un nombre réduit de caméras. Il s’agit d’estimer la vraisemblance de la reconnaissance des gestes en fonction des conditions d’observation. Notre méthode consiste `a classifier un ensemble de gestes `a partir de l’apprentissage de descripteurs de mouvement. Les descripteurs de mouvement sont des signatures locales du mouvement de points d’intérêt associés aux descriptions locales de la texture du voisinage des points considérés. L’approche a été validée sur une base de données de gestes publique KTH et des résultats encourageants ont été obtenusIn this thesis, we aim to recognize gestures (e. G. Hand raising) and more generally short actions (e. G. Fall, bending) accomplished by an individual. Many techniques have already been proposed for gesture recognition in specific environment (e. G. Laboratory) using the cooperation of several sensors (e. G. Camera network, individual equipped with markers). Despite these strong hypotheses, gesture recognition is still brittle and often depends on the position of the individual relatively to the cameras. We propose to reduce these hypotheses in order to conceive general algorithm enabling the recognition of the gesture of an individual involving in an unconstrained environment and observed through limited number of cameras. The goal is to estimate the likelihood of gesture recognition in function of the observation conditions. Our method consists of classifying a set of gestures by learning motion descriptors. These motion descriptors are local signatures of the motion of corner points which are associated with their local textural description. We demonstrate the effectiveness of our motion descriptors by recognizing the actions of the public KTH database
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Semprini, Mattia. "Gesture Recognition: una panoramica." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15672/.
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Per decenni, l’uomo ha interagito con i calcolatori e altri dispositivi quasi esclusivamente premendo i tasti e facendo "click" sul mouse. Al giorno d’oggi, vi è un grande cambiamento in atto a seguito di una ondata di nuove tecnologie che rispondono alle azioni più naturali, come il movimento delle mani o dell’intero corpo. Il mercato tecnologico è stato scosso in un primo momento dalla sostituzione delle tecniche di interazione standard con approcci di tipo "touch and motion sensing"; il passo successivo è l’introduzione di tecniche e tecnologie che permettano all’utente di accedere e manipolare informazioni interagendo con un sistema informatico solamente con gesti ed azioni del corpo. A questo proposito nasce la Gesture Recognition, una parte sostanziale dell’informatica e della tecnologia del linguaggio, che ha come obbiettivo quello di interpretare ed elaborare gesti umani attraverso algoritmi informatici. In questa trattazione andrò a spiegare, nei primi due capitoli la storia delle tecnologie Wearable dai primi orologi che non si limitavano alla sola indicazione dell’orario fino alla nascita dei sistemi utilizzati al giorno d’oggi per la Gesture Recognition. Segue, nel terzo capitolo, un’esposizione dei più utilizzati algoritmi di classificazione delle gesture. Nel quarto andrò ad approfondire uno dei primi framework progettati per fare in modo che lo sviluppatore si concentri sull’applicazione tralasciando la parte di codifica e classificazione delle gesture. Nell’ultima parte verrà esaminato uno dei dispositivi più performanti ed efficaci in questo campo: il Myo Armband. Saranno riportate anche due studi che dimostrano la sua validità.
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Gingir, Emrah. "Hand Gesture Recognition System." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612532/index.pdf.
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This thesis study presents a hand gesture recognition system, which replaces input devices like keyboard and mouse with static and dynamic hand gestures, for interactive computer applications. Despite the increase in the attention of such systems there are still certain limitations in literature. Most applications require different constraints like having distinct lightning conditions, usage of a specific camera, making the user wear a multi-colored glove or need lots of training data. The system mentioned in this study disables all these restrictions and provides an adaptive, effort free environment to the user. Study starts with an analysis of the different color space performances over skin color extraction. This analysis is independent of the working system and just performed to attain valuable information about the color spaces. Working system is based on two steps, namely hand detection and hand gesture recognition. In the hand detection process, normalized RGB color space skin locus is used to threshold the coarse skin pixels in the image. Then an adaptive skin locus, whose varying boundaries are estimated from coarse skin region pixels, segments the distinct skin color in the image for the current conditions. Since face has a distinct shape, face is detected among the connected group of skin pixels by using the shape analysis. Non-face connected group of skin pixels are determined as hands. Gesture of the hand is recognized by improved centroidal profile method, which is applied around the detected hand. A 3D flight war game, a boxing game and a media player, which are controlled remotely by just using static and dynamic hand gestures, were developed as human machine interface applications by using the theoretical background of this study. In the experiments, recorded videos were used to measure the performance of the system and a correct recognition rate of ~90% was acquired with nearly real time computation.
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Dang, Darren Phi Bang. "Template based gesture recognition." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/41404.
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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (p. 65-66).
by Darren PHi Bang Dang.
M.S.
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Wang, Lei. "Personalized Dynamic Hand Gesture Recognition." Thesis, KTH, Medieteknik och interaktionsdesign, MID, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231345.
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Human gestures, with the spatial-temporal variability, are difficult to be recognized by a generic model or classifier that are applicable for everyone. To address the problem, in this thesis, personalized dynamic gesture recognition approaches are proposed. Specifically, based on Dynamic Time Warping(DTW), a novel concept of Subject Relation Network is introduced to describe the similarity of subjects in performing dynamic gestures, which offers a brand new view for gesture recognition. By clustering or arranging training subjects based on the network, two personalization algorithms are proposed respectively for generative models and discriminative models. Moreover, three basic recognition methods, DTW-based template matching, Hidden Markov Model(HMM) and Fisher Vector combining classification, are compared and integrated into the proposed personalized gesture recognition. The proposed approaches are evaluated on a challenging dynamic hand gesture recognition dataset DHG14/28, which contains the depth images and skeleton coordinates returned by the Intel RealSense depth camera. Experimental results show that the proposed personalized algorithms can significantly improve the performance of basic generative&discriminative models and achieve the state-of-the-art accuracy of 86.2%.Människliga gester, med spatiala/temporala variationer, är svåra att känna igen med en generisk modell eller klassificeringsmetod. För att komma till rätta med problemet, föreslås personifierade, dynamiska gest igenkänningssätt baserade på Dynamisk Time Warping (DTW) och ett nytt koncept: Subjekt-Relativt Nätverk för att beskriva likheter vid utförande av dynamiska gester, vilket ger en ny syn på gest igenkänning. Genom att klustra eller ordna träningssubjekt baserat på nätverket föreslås två personifieringsalgoritmer för generativa och diskriminerande modeller. Dessutom jämförs och integreras tre grundläggande igenkänningsmetoder, DTW-baserad mall-matchning, Hidden Markov Model (HMM) och Fisher Vector-klassificering i den föreslagna personifierade gestigenkännande ansatsen. De föreslagna tillvägagångssätten utvärderas på ett utmanande, dynamiskt handmanipulerings dataset DHG14/28, som innehåller djupbilderna och skelettkoordinaterna som returneras av Intels RealSense-djupkamera. Experimentella resultat visar att de föreslagna personifierade algoritmerna kan förbättra prestandan i jämfört medgrundläggande generativa och diskriminerande modeller och uppnå den högsta nivån på 86,2%.
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Espinoza, Victor. "Gesture Recognition in Tennis Biomechanics." Master's thesis, Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/530096.
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Electrical and Computer EngineeringM.S.E.E.
The purpose of this study is to create a gesture recognition system that interprets motion capture data of a tennis player to determine which biomechanical aspects of a tennis swing best correlate to a swing efficacy. For our learning set this work aimed to record 50 tennis athletes of similar competency with the Microsoft Kinect performing standard tennis swings in the presence of different targets. With the acquired data we extracted biomechanical features that hypothetically correlated to ball trajectory using proper technique and tested them as sequential inputs to our designed classifiers. This work implements deep learning algorithms as variable-length sequence classifiers, recurrent neural networks (RNN), to predict tennis ball trajectory. In attempt to learn temporal dependencies within a tennis swing, we implemented gate-augmented RNNs. This study compared the RNN to two gated models; gated recurrent units (GRU), and long short-term memory (LSTM) units. We observed similar classification performance across models while the gated-methods reached convergence twice as fast as the baseline RNN. The results displayed 1.2 entropy loss and 50 % classification accuracy indicating that the hypothesized biomechanical features were loosely correlated to swing efficacy or that they were not accurately depicted by the sensor
Temple University--Theses
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Nygård, Espen Solberg. "Multi-touch Interaction with Gesture Recognition." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9126.
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This master's thesis explores the world of multi-touch interaction with gesture recognition. The focus is on camera based multi-touch techniques, as these provide a new dimension to multi-touch with its ability to recognize objects. During the project, a multi-touch table based on the technology Diffused Surface Illumination has been built. In addition to building a table, a complete gesture recognition system has been implemented, and different gesture recognition algorithms have been successfully tested in a multi-touch environment. The goal with this table, and the accompanying gesture recognition system, is to create an open and affordable multi-touch solution, with the purpose of bringing multi-touch out to the masses. By doing this, more people will be able to enjoy the benefits of a more natural interaction with computers. In a larger perspective, multi-touch is just the beginning, and by adding additional modalities to our applications, such as speech recognition and full body tracking, a whole new level of computer interaction will be possible.
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Khan, Muhammad. "Hand Gesture Detection & Recognition System." Thesis, Högskolan Dalarna, Datateknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:du-6496.
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The project introduces an application using computer vision for Hand gesture recognition. A camera records a live video stream, from which a snapshot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least once. After that a test gesture is given to it and the system tries to recognize it.A research was carried out on a number of algorithms that could best differentiate a hand gesture. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algorithm removes the background of each training gesture. After that the image is converted into a binary image and the sums of all diagonal elements of the picture are taken. This sum helps us in differentiating and classifying different hand gestures.Previous systems have used data gloves or markers for input in the system. I have no such constraints for using the system. The user can give hand gestures in view of the camera naturally. A completely robust hand gesture recognition system is still under heavy research and development; the implemented system serves as an extendible foundation for future work.
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Glatt, Ruben [UNESP]. "Deep learning architecture for gesture recognition." Universidade Estadual Paulista (UNESP), 2014. http://hdl.handle.net/11449/115718.
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000807195.pdf: 2462524 bytes, checksum: 91686fbe11c74337c40fe57671eb8d82 (MD5)O reconhecimento de atividade de visão de computador desempenha um papel importante na investigação para aplicações como interfaces humanas de computador, ambientes inteligentes, vigilância ou sistemas médicos. Neste trabalho, é proposto um sistema de reconhecimento de gestos com base em uma arquitetura de aprendizagem profunda. Ele é usado para analisar o desempenho quando treinado com os dados de entrada multi-modais em um conjunto de dados de linguagem de sinais italiana. A área de pesquisa subjacente é um campo chamado interação homem-máquina. Ele combina a pesquisa sobre interfaces naturais, reconhecimento de gestos e de atividade, aprendizagem de máquina e tecnologias de sensores que são usados para capturar a entrada do meio ambiente para processamento posterior. Essas áreas são introduzidas e os conceitos básicos são descritos. O ambiente de desenvolvimento para o pré-processamento de dados e algoritmos de aprendizagem de máquina programada em Python é descrito e as principais bibliotecas são discutidas. A coleta dos fluxos de dados é explicada e é descrito o conjunto de dados utilizado. A arquitetura proposta de aprendizagem consiste em dois passos. O pré-processamento dos dados de entrada e a arquitetura de aprendizagem. O pré-processamento é limitado a três estratégias diferentes, que são combinadas para oferecer seis diferentes perfis de préprocessamento. No segundo passo, um Deep Belief Network é introduzido e os seus componentes são explicados. Com esta definição, 294 experimentos são realizados com diferentes configurações. As variáveis que são alteradas são as definições de pré-processamento, a estrutura de camadas do modelo, a taxa de aprendizagem de pré-treino e a taxa de aprendizagem de afinação. A avaliação dessas experiências mostra que a abordagem de utilização de uma arquitetura ... (Resumo completo, clicar acesso eletrônico abaixo)
Activity recognition from computer vision plays an important role in research towards applications like human computer interfaces, intelligent environments, surveillance or medical systems. In this work, a gesture recognition system based on a deep learning architecture is proposed. It is used to analyze the performance when trained with multi-modal input data on an Italian sign language dataset. The underlying research area is a field called human-machine interaction. It combines research on natural user interfaces, gesture and activity recognition, machine learning and sensor technologies, which are used to capture the environmental input for further processing. Those areas are introduced and the basic concepts are described. The development environment for preprocessing data and programming machine learning algorithms with Python is described and the main libraries are discussed. The gathering of the multi-modal data streams is explained and the used dataset is outlined. The proposed learning architecture consists of two steps. The preprocessing of the input data and the actual learning architecture. The preprocessing is limited to three different strategies, which are combined to offer six different preprocessing profiles. In the second step, a Deep Belief network is introduced and its components are explained. With this setup, 294 experiments are conducted with varying configuration settings. The variables that are altered are the preprocessing settings, the layer structure of the model, the pretraining and the fine-tune learning rate. The evaluation of these experiments show that the approach of using a deep learning architecture on an activity or gesture recognition task yields acceptable results, but has not yet reached a level of maturity, which would allow to use the developed models in serious applications.
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Gillian, N. E. "Gesture recognition for musician computer interaction." Thesis, Queen's University Belfast, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.546348.
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Cairns, Alistair Y. "Towards the automatic recognition of gesture." Thesis, University of Dundee, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385803.
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Harding, Peter Reginald George. "Gesture recognition by Fourier analysis techniques." Thesis, City University London, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440735.
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Tanguay, Donald O. (Donald Ovila). "Hidden Markov models for gesture recognition." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/37796.
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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 41-42).
by Donald O. Tanguay, Jr.
M.Eng.
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Yao, Yi. "Hand gesture recognition in uncontrolled environments." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/74268/.
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Human Computer Interaction has been relying on mechanical devices to feed information into computers with low efficiency for a long time. With the recent developments in image processing and machine learning methods, the computer vision community is ready to develop the next generation of Human Computer Interaction methods, including Hand Gesture Recognition methods. A comprehensive Hand Gesture Recognition based semantic level Human Computer Interaction framework for uncontrolled environments is proposed in this thesis. The framework contains novel methods for Hand Posture Recognition, Hand Gesture Recognition and Hand Gesture Spotting. The Hand Posture Recognition method in the proposed framework is capable of recognising predefined still hand postures from cluttered backgrounds. Texture features are used in conjunction with Adaptive Boosting to form a novel feature selection scheme, which can effectively detect and select discriminative texture features from the training samples of the posture classes. A novel Hand Tracking method called Adaptive SURF Tracking is proposed in this thesis. Texture key points are used to track multiple hand candidates in the scene. This tracking method matches texture key points of hand candidates within adjacent frames to calculate the movement directions of hand candidates. With the gesture trajectories provided by the Adaptive SURF Tracking method, a novel classi�er called Partition Matrix is introduced to perform gesture classification for uncontrolled environments with multiple hand candidates. The trajectories of all hand candidates extracted from the original video under different frame rates are used to analyse the movements of hand candidates. An alternative gesture classifier based on Convolutional Neural Network is also proposed. The input images of the Neural Network are approximate trajectory images reconstructed from the tracking results of the Adaptive SURF Tracking method. For Hand Gesture Spotting, a forward spotting scheme is introduced to detect the starting and ending points of the prede�ned gestures in the continuously signed gesture videos. A Non-Sign Model is also proposed to simulate meaningless hand movements between the meaningful gestures. The proposed framework can perform well with unconstrained scene settings, including frontal occlusions, background distractions and changing lighting conditions. Moreover, it is invariant to changing scales, speed and locations of the gesture trajectories.
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Glatt, Ruben. "Deep learning architecture for gesture recognition /." Guaratinguetá, 2014. http://hdl.handle.net/11449/115718.
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Orientador: José Celso Freire JuniorCoorientador: Daniel Julien Barros da Silva Sampaio
Banca: Galeno José de Sena
Banca: Luiz de Siqueira Martins Filho
Resumo: O reconhecimento de atividade de visão de computador desempenha um papel importante na investigação para aplicações como interfaces humanas de computador, ambientes inteligentes, vigilância ou sistemas médicos. Neste trabalho, é proposto um sistema de reconhecimento de gestos com base em uma arquitetura de aprendizagem profunda. Ele é usado para analisar o desempenho quando treinado com os dados de entrada multi-modais em um conjunto de dados de linguagem de sinais italiana. A área de pesquisa subjacente é um campo chamado interação homem-máquina. Ele combina a pesquisa sobre interfaces naturais, reconhecimento de gestos e de atividade, aprendizagem de máquina e tecnologias de sensores que são usados para capturar a entrada do meio ambiente para processamento posterior. Essas áreas são introduzidas e os conceitos básicos são descritos. O ambiente de desenvolvimento para o pré-processamento de dados e algoritmos de aprendizagem de máquina programada em Python é descrito e as principais bibliotecas são discutidas. A coleta dos fluxos de dados é explicada e é descrito o conjunto de dados utilizado. A arquitetura proposta de aprendizagem consiste em dois passos. O pré-processamento dos dados de entrada e a arquitetura de aprendizagem. O pré-processamento é limitado a três estratégias diferentes, que são combinadas para oferecer seis diferentes perfis de préprocessamento. No segundo passo, um Deep Belief Network é introduzido e os seus componentes são explicados. Com esta definição, 294 experimentos são realizados com diferentes configurações. As variáveis que são alteradas são as definições de pré-processamento, a estrutura de camadas do modelo, a taxa de aprendizagem de pré-treino e a taxa de aprendizagem de afinação. A avaliação dessas experiências mostra que a abordagem de utilização de uma arquitetura ... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: Activity recognition from computer vision plays an important role in research towards applications like human computer interfaces, intelligent environments, surveillance or medical systems. In this work, a gesture recognition system based on a deep learning architecture is proposed. It is used to analyze the performance when trained with multi-modal input data on an Italian sign language dataset. The underlying research area is a field called human-machine interaction. It combines research on natural user interfaces, gesture and activity recognition, machine learning and sensor technologies, which are used to capture the environmental input for further processing. Those areas are introduced and the basic concepts are described. The development environment for preprocessing data and programming machine learning algorithms with Python is described and the main libraries are discussed. The gathering of the multi-modal data streams is explained and the used dataset is outlined. The proposed learning architecture consists of two steps. The preprocessing of the input data and the actual learning architecture. The preprocessing is limited to three different strategies, which are combined to offer six different preprocessing profiles. In the second step, a Deep Belief network is introduced and its components are explained. With this setup, 294 experiments are conducted with varying configuration settings. The variables that are altered are the preprocessing settings, the layer structure of the model, the pretraining and the fine-tune learning rate. The evaluation of these experiments show that the approach of using a deep learning architecture on an activity or gesture recognition task yields acceptable results, but has not yet reached a level of maturity, which would allow to use the developed models in serious applications.
Mestre
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Caceres, Carlos Antonio. "Machine Learning Techniques for Gesture Recognition." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/52556.
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Classification of human movement is a large field of interest to Human-Machine Interface researchers. The reason for this lies in the large emphasis humans place on gestures while communicating with each other and while interacting with machines. Such gestures can be digitized in a number of ways, including both passive methods, such as cameras, and active methods, such as wearable sensors. While passive methods might be the ideal, they are not always feasible, especially when dealing in unstructured environments. Instead, wearable sensors have gained interest as a method of gesture classification, especially in the upper limbs. Lower arm movements are made up of a combination of multiple electrical signals known as Motor Unit Action Potentials (MUAPs). These signals can be recorded from surface electrodes placed on the surface of the skin, and used for prosthetic control, sign language recognition, human machine interface, and a myriad of other applications.
In order to move a step closer to these goal applications, this thesis compares three different machine learning tools, which include Hidden Markov Models (HMMs), Support Vector Machines (SVMs), and Dynamic Time Warping (DTW), to recognize a number of different gestures classes. It further contrasts the applicability of these tools to noisy data in the form of the Ninapro dataset, a benchmarking tool put forth by a conglomerate of universities. Using this dataset as a basis, this work paves a path for the analysis required to optimize each of the three classifiers. Ultimately, care is taken to compare the three classifiers for their utility against noisy data, and a comparison is made against classification results put forth by other researchers in the field.
The outcome of this work is 90+ % recognition of individual gestures from the Ninapro dataset whilst using two of the three distinct classifiers. Comparison against previous works by other researchers shows these results to outperform all other thus far. Through further work with these tools, an end user might control a robotic or prosthetic arm, or translate sign language, or perhaps simply interact with a computer.Master of Science
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Pfister, Tomas. "Advancing human pose and gesture recognition." Thesis, University of Oxford, 2015. http://ora.ox.ac.uk/objects/uuid:64e5b1be-231e-49ed-b385-e87db6dbeed8.
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This thesis presents new methods in two closely related areas of computer vision: human pose estimation, and gesture recognition in videos. In human pose estimation, we show that random forests can be used to estimate human pose in monocular videos. To this end, we propose a co-segmentation algorithm for segmenting humans out of videos, and an evaluator that predicts whether the estimated poses are correct or not. We further extend this pose estimator to new domains (with a transfer learning approach), and enhance its predictions by predicting the joint positions sequentially (rather than independently) in an image, and using temporal information in the videos (rather than predicting the poses from a single frame). Finally, we go beyond random forests, and show that convolutional neural networks can be used to estimate human pose even more accurately and efficiently. We propose two new convolutional neural network architectures, and show how optical flow can be employed in convolutional nets to further improve the predictions. In gesture recognition, we explore the idea of using weak supervision to learn gestures. We show that we can learn sign language automatically from signed TV broadcasts with subtitles by letting algorithms 'watch' the TV broadcasts and 'match' the signs with the subtitles. We further show that if even a small amount of strong supervision is available (as there is for sign language, in the form of sign language video dictionaries), this strong supervision can be combined with weak supervision to learn even better models.
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Al-Rajab, Moaath. "Hand gesture recognition for multimedia applications." Thesis, University of Leeds, 2008. http://etheses.whiterose.ac.uk/607/.
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Hand gesture is potentially a very natural and useful modality for human-machine interaction. It is considered to be one of the most complicated and interesting challenges in computer vision due to its articulated structure and environmental variations. Solving such challenges requires robust hand detection, feature description, and viewpoint invariant classification. This thesis introduces several steps to tackle these challenges and applies them in a hand-gesture-based application (a game) to demonstrate the proposed approach. Techniques on new feature description, hand gesture detection and viewpoint invariant methods are explored and evaluated. A normal webcam is used in the research as input device. Hands are segmented using pre-trained skin colour models and tracked using the CAMShift tracker. Moment invariants are used as a shape descriptor. A new approach utilising the Zernike Velocity Moments (ZVMs, first introduced by Shutler and Nixon [1,2]), is examined on hand gestures. Results obtained using the ZVMs as spatial-temporal descriptor are compared to an HMM with Zemike moments (ZMs). Manually isolated hand gestures are used as input to the ZVM descriptor which generates vectors of features that are classified using a regression classifier. The performance of ZVM is evaluated using isolated, user-independent and user-dependent data. Isolating (segmenting) the gesture manually from a video stream for gesture recognition is a research proposition only and real life scenarios require an automatic hand gesture detection mechanism. Two methods for detecting gestures are examined. Firstly, hand gesture detection is performed using a sliding window which segments sequences of frames and then evaluates them against pre-trained HMMs. Secondly, the set of class-specific HMMs is combined into a single HMM and the Viterbi algorithm is then used to find the optimal sequence of gestures. Finally, the thesis proposes a flexible application that provides the user with options to perform the gesture from different viewpoints. A usable hand gesture recognition system should be able to cope with such viewpoint variations. To solve this problem, a new approach is introduced which makes use of 3D models of hand gestures (not postures) for generating projections. A virtual arm with 3D models of real hands is created. After that, virtual movements of the hand are simulated using animation software and projected from different viewpoints. Using a multi-Gaussian HMM, the system is trained on the projected sequences. Each set of hand gesture projections is marked with its specific class and used to train the single multi-class HMNI with gestures across different viewpoints.
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Jia, Jia. "Interactive Imaging via Hand Gesture Recognition." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/4259.
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With the growth of computer power, Digital Image Processing plays a more and more important role in the modern world, including the field of industry, medical, communications, spaceflight technology etc. As a sub-field, Interactive Image Processing emphasizes particularly on the communications between machine and human. The basic flowchart is definition of object, analysis and training phase, recognition and feedback. Generally speaking, the core issue is how we define the interesting object and track them more accurately in order to complete the interaction process successfully.
This thesis proposes a novel dynamic simulation scheme for interactive image processing. The work consists of two main parts: Hand Motion Detection and Hand Gesture recognition. Within a hand motion detection processing, movement of hand will be identified and extracted. In a specific detection period, the current image is compared with the previous image in order to generate the difference between them. If the generated difference exceeds predefined threshold alarm, a typical hand motion movement is detected. Furthermore, in some particular situations, changes of hand gesture are also desired to be detected and classified. This task requires features extraction and feature comparison among each type of gestures. The essentials of hand gesture are including some low level features such as color, shape etc. Another important feature is orientation histogram. Each type of hand gestures has its particular representation in the domain of orientation histogram. Because Gaussian Mixture Model has great advantages to represent the object with essential feature elements and the Expectation-Maximization is the efficient procedure to compute the maximum likelihood between testing images and predefined standard sample of each different gesture, the comparability between testing image and samples of each type of gestures will be estimated by Expectation-Maximization algorithm in Gaussian Mixture Model. The performance of this approach in experiments shows the proposed method works well and accurately.
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Toure, Zikra. "Human-Machine Interface Using Facial Gesture Recognition." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc1062841/.
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This Master thesis proposes a human-computer interface for individual with limited hand movements that incorporate the use of facial gesture as a means of communication. The system recognizes faces and extracts facial gestures to map them into Morse code that would be translated in English in real time. The system is implemented on a MACBOOK computer using Python software, OpenCV library, and Dlib library. The system is tested by 6 students. Five of the testers were not familiar with Morse code. They performed the experiments in an average of 90 seconds. One of the tester was familiar with Morse code and performed the experiment in 53 seconds. It is concluded that errors occurred due to variations in features of the testers, lighting conditions, and unfamiliarity with the system. Implementing an auto correction and auto prediction system will decrease typing time considerably and make the system more robust.
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Liu, Nianjun. "Hand gesture recognition by Hidden Markov Models /." [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18158.pdf.
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Pun, James Chi-Him. "Gesture recognition with application in music arrangement." Diss., University of Pretoria, 2006. http://upetd.up.ac.za/thesis/available/etd-11052007-171910/.
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Chan, Siu Chi 1979. "Hand and fingertip tracking for gesture recognition." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=83855.
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A hand gesture interface allows for seamless interaction with both virtual and physical objects in computer augmented environments. However, developing a reliable hand-pose detection and recognition system using computer vision remain to be a challenging problem. In this thesis, two tracking systems relying on different image features are described and compared. The first system employs skin color to extract skin regions from an image. Then, a user's hand is located by using a circle fitting algorithms inside the largest skin blob. To find fingertips, a circular Hough transform is applied to the hand contour and followed by a filtering process to remove false positives. For the second system, instead of relying on skin color which is susceptible to changing lighting conditions, it focuses on processing edge information. Foreground objects are extracted using edge detection and motion analysis. To find a user's hand, a wrist template is constructed to model a wrist's shape and tracked with the CONDENSATION algorithm. Once the wrist configuration in known, fingertips are detected in the edge image by using a combination of circular Hough transform and importance sampling. Experimental results are employed to compare the performance of both systems.
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Kolesnik, Paul. "Conducting gesture recognition, analysis and performance system." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=81499.
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A number of conducting gesture analysis and performance systems have been developed over the years. However, most of the previous projects either primarily concentrated on tracking tempo and amplitude indicating gestures, or implemented individual mapping techniques for expressive gestures that varied from research to research. There is a clear need for a uniform process that could be applied toward analysis of both indicative and expressive gestures. The proposed system provides a set of tools that contain extensive functionality for identification, classification and performance with conducting gestures. Gesture recognition procedure is designed on the basis of Hidden Markov Model (HMM) process. A set of HMM tools are developed for Max/MSP software. Training and recognition procedures are applied toward both right-hand beat- and amplitude-indicative gestures, and left-hand expressive gestures. Continuous recognition of right-hand gestures is incorporated into a real-time gesture analysis and performance system in Eyesweb and Max/MSP/Jitter environments.
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King, Rachel C. "Hand gesture recognition for minimally invasive surgery." Thesis, Imperial College London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.497748.
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Puranam, Muthukumar B. "Towards Full-Body Gesture Analysis and Recognition." UKnowledge, 2005. http://uknowledge.uky.edu/gradschool_theses/227.
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With computers being embedded in every walk of our life, there is an increasing demand forintuitive devices for human-computer interaction. As human beings use gestures as importantmeans of communication, devices based on gesture recognition systems will be effective for humaninteraction with computers. However, it is very important to keep such a system as non-intrusive aspossible, to reduce the limitations of interactions. Designing such non-intrusive, intuitive, camerabasedreal-time gesture recognition system has been an active area of research research in the fieldof computer vision.Gesture recognition invariably involves tracking body parts. We find many research works intracking body parts like eyes, lips, face etc. However, there is relatively little work being done onfull body tracking. Full-body tracking is difficult because it is expensive to model the full-body aseither 2D or 3D model and to track its movements.In this work, we propose a monocular gesture recognition system that focuses on recognizing a setof arm movements commonly used to direct traffic, guiding aircraft landing and for communicationover long distances. This is an attempt towards implementing gesture recognition systems thatrequire full body tracking, for e.g. an automated recognition semaphore flag signaling system.We have implemented a robust full-body tracking system, which forms the backbone of ourgesture analyzer. The tracker makes use of two dimensional link-joint (LJ) model, which representsthe human body, for tracking. Currently, we track the movements of the arms in a video sequence,however we have future plans to make the system real-time. We use distance transform techniquesto track the movements by fitting the parameters of LJ model in every frames of the video captured.The tracker's output is fed a to state-machine which identifies the gestures made. We haveimplemented this system using four sub-systems. Namely1. Background subtraction sub-system, using Gaussian models and median filters.2. Full-body Tracker, using L-J Model APIs3. Quantizer, that converts tracker's output into defined alphabets4. Gesture analyzer, that reads the alphabets into action performed.Currently, our gesture vocabulary contains gestures involving arms moving up and down which canbe used for detecting semaphore, flag signaling system. Also we can detect gestures like clappingand waving of arms.
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Zanghieri, Marcello. "sEMG-based hand gesture recognition with deep learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18112/.
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Hand gesture recognition based on surface electromyographic (sEMG) signals is a promising approach for the development of Human-Machine Interfaces (HMIs) with a natural control, such as intuitive robot interfaces or poly-articulated prostheses. However, real-world applications are limited by reliability problems due to motion artifacts, postural and temporal variability, and sensor re-positioning.
This master thesis is the first application of deep learning on the Unibo-INAIL dataset, the first public sEMG dataset exploring the variability between subjects, sessions and arm postures, by collecting data over 8 sessions of each of 7 able-bodied subjects executing 6 hand gestures in 4 arm postures. In the most recent studies, the variability is addressed with training strategies based on training set composition, which improve inter-posture and inter-day generalization of classical (i.e. non-deep) machine learning classifiers, among which the RBF-kernel SVM yields the highest accuracy.
The deep architecture realized in this work is a 1d-CNN implemented in Pytorch, inspired by a 2d-CNN reported to perform well on other public benchmark databases. On this 1d-CNN, various training strategies based on training set composition were implemented and tested.
Multi-session training proves to yield higher inter-session validation accuracies than single-session training. Two-posture training proves to be the best postural training (proving the benefit of training on more than one posture), and yields 81.2% inter-posture test accuracy. Five-day training proves to be the best multi-day training, and yields 75.9% inter-day test accuracy. All results are close to the baseline. Moreover, the results of multi-day trainings highlight the phenomenon of user adaptation, indicating that training should also prioritize recent data.
Though not better than the baseline, the achieved classification accuracies rightfully place the 1d-CNN among the candidates for further research.
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Bernard, Arnaud Jean Marc. "Human computer interface based on hand gesture recognition." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/42748.
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With the improvement of multimedia technologies such as broadband-enabled HDTV, video on demand and internet TV, the computer and the TV are merging to become a single device. Moreover the previously cited technologies as well as DVD or Blu-ray can provide menu navigation and interactive content.
The growing interest in video conferencing led to the integration of the webcam in different devices such as laptop, cell phones and even the TV set. Our approach is to directly use an embedded webcam to remotely control a TV set using hand gestures. Using specific gestures, a user is able to control the TV. A dedicated interface can then be used to select a TV channel, adjust volume or browse videos from an online streaming server.
This approach leads to several challenges. The first is the use of a simple webcam which leads to a vision based system. From the single webcam, we need to recognize the hand and identify its gesture or trajectory. A TV set is usually installed in a living room which implies constraints such as a potentially moving background and luminance change. These issues will be further discussed as well as the methods developed to resolve them. Video browsing is one example of the use of gesture recognition. To illustrate another application, we developed a simple game controlled by hand gestures.
The emergence of 3D TVs is allowing the development of 3D video conferencing. Therefore we also consider the use of a stereo camera to recognize hand gesture.
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Zhu, Hong Min. "Real-time hand gesture recognition using motion tracking." Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2182870.
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Wilson, Andrew David. "Adaptive models for the recognition of human gesture." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/62951.
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Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2000.Includes bibliographical references (leaves 135-140).
Tomorrow's ubiquitous computing environments will go beyond the keyboard, mouse and monitor paradigm of interaction and will require the automatic interpretation of human motion using a variety of sensors including video cameras. I present several techniques for human motion recognition that are inspired by observations on human gesture, the class of communicative human movement. Typically, gesture recognition systems are unable to handle systematic variation in the input signal, and so are too brittle to be applied successfully in many real-world situations. To address this problem, I present modeling and recognition techniques to adapt gesture models to the situation at hand. A number of systems and frameworks that use adaptive gesture models are presented. First, the parametric hidden Markov model (PHMM) addresses the representation and recognition of gesture families, to extract how a gesture is executed. Second, strong temporal models drawn from natural gesture theory are exploited to segment two kinds of natural gestures from video sequences. Third, a realtime computer vision system learns gesture models online from time-varying context. Fourth, a realtime computer vision system employs hybrid Bayesian networks to unify and extend the previous approaches, as well as point the way for future work.
by Andrew David Wilson.
Ph.D.
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Moy, Milyn C. (Milyn Cecilia) 1975. "Real-time hand gesture recognition in complex environments." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50054.
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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (leaves 65-68).
by Milyn C. Moy.
S.B.and M.Eng.
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Bailey, Sam. "Interactive exploration of historic information via gesture recognition." Thesis, University of East Anglia, 2012. https://ueaeprints.uea.ac.uk/42540/.
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Developers of interactive exhibits often struggle to �nd appropriate input devices that enable intuitive control, permitting the visitors to engage e�ectively with the content. Recently motion sensing input devices like the Microsoft Kinect or Panasonic D-Imager have become available enabling gesture based control of computer systems. These devices present an attractive input device for exhibits since the user can interact with their hands and they are not required to physically touch any part of the system. In this thesis we investigate techniques to enable the raw data coming from these types of devices to be used to control an interactive exhibit. Object recognition and tracking techniques are used to analyse the user's hand where movement and clicks are processed. To show the e�ectiveness of the techniques the gesture system is used to control an interactive system designed to inform the public about iconic buildings in the centre of Norwich, UK. We evaluate two methods of making selections in the test environment. At the time of experimentation the technologies were relatively new to the image processing environment. As a result of the research presented in this thesis, the techniques and methods used have been detailed and published [3] at the VSMM (Virtual Systems and Multimedia 2012) conference with the intention of further forwarding the area.
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李世淵. "Anti-Gesture Model For Gesture Recognition." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/88514897535275004323.
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Tsai, Jui-Che, and 蔡睿哲. "Hand Gesture Recognition." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/e6nbcb.
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碩士亞東技術學院
資訊與通訊工程研究所
100
In recent years, image processing has been developed for a long time. Hand recognition systems attract many researchers. In this paper, using a easy hand gesture recognition algorithm reduces the amount of data and obtains the desired result. First of all, the computer gets two pictures by a webcam we set up. The resolution of pictures are set as 320*240. The background subtraction method from two pictures is used to reduce the amount of data. Then, erosion and dilation methods are used to reduce the noise. The remaining image is only the hand region. Then we find the centroid of the hand region. According to the centroid, we search the right-most and the left-most coordinates and to make a record. The distance from the centroid to the right-most and the left-most becomes a radius. We draw a circle by the radius to remove the connection by fingers and finally give a mark on the last image to find the sum of fingers.
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Lemarcis, Baptiste. "Towards streaming gesture recognition." Thèse, 2016. http://constellation.uqac.ca/4132/1/Lemarcis_uqac_0862N_10294.pdf.
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The emergence of low-cost sensors allows more devices to be equipped with various types of sensors. In this way, mobile device such as smartphones or smartwatches now may contain accelerometers, gyroscopes, etc. This offers new possibilities for interacting with the environment and benefits would come to exploit these sensors. As a consequence, the literature on gesture recognition systems that employ such sensors grow considerably. The literature regarding online gesture recognition counts many methods based on Dynamic Time Warping (DTW). However, this method was demonstrated has non-efficient for time series from inertial sensors unit as a lot of noise is present. In this way new methods based on LCSS (Longest Common SubSequence) were introduced. Nevertheless, none of them focus on a class optimization process. In this master thesis, we present and evaluate a new algorithm for online gesture recognition in noisy streams. This technique relies upon the LM-WLCSS (Limited Memory and Warping LCSS) algorithm that has demonstrated its efficiency on gesture recognition. This new method involves a quantization step (via the K-Means clustering algorithm) that transforms new data to a finite set. In this way, each new sample can be compared to several templates (one per class). Gestures are rejected based on a previously trained rejection threshold. Thereafter, an algorithm, called SearchMax, find a local maximum within a sliding window and output whether or not the gesture has been recognized. In order to resolve conflicts that may occur, another classifier (i.e. C4.5) could be completed. As the K-Means clustering algorithm needs to be initialized with the number of clusters to create, we also introduce a straightforward optimization process. Such an operation also optimizes the window size for the SearchMax algorithm. In order to demonstrate the robustness of our algorithm, an experiment has been performed over two different data sets. However, results on tested data sets are only accurate when training data are used as test data. This may be due to the fact that the method is in an overlearning state.
L’apparition de nouveaux capteurs à bas prix a permis d’en équiper dans beaucoup plus d’appareils. En effet, dans les appareils mobiles tels que les téléphones et les montres intelligentes nous retrouvons des accéléromètres, gyroscopes, etc. Ces capteurs présents dans notre vie quotidienne offrent de toutes nouvelles possibilités en matière d’interaction avec notre environnement et il serait avantageux de les utiliser. Cela a eu pour conséquence une augmentation considérable du nombre de recherches dans le domaine de reconnaissance de geste basé sur ce type de capteur. La littérature concernant la reconnaissance de gestes en ligne comptabilise beaucoup de méthodes qui se basent sur Dynamic Time Warping (DTW). Cependant, il a été démontré que cette méthode se révèle inefficace en ce qui concerne les séries temporelles provenant d’une centrale à inertie puisqu’elles contiennent beaucoup de bruit. En ce sens de nouvelles méthodes basées sur LCSS (Longest Common SubSequence) sont apparues. Néanmoins, aucune d’entre elles ne s’est focalisée sur un processus d’optimisation par class. Ce mémoire de maîtrise consiste en une présentation et une évaluation d’un nouvel algorithme pour la reconnaissance de geste en ligne avec des données bruitées. Cette technique repose sur l’algorithme LM-WLCSS (Limited Memory and Warping LCSS) qui a d’ores et déjà démontré son efficacité quant à la reconnaissance de geste. Cette nouvelle méthode est donc composée d’une étape dite de quantification (grâce à l’algorithme de regroupement K-Means) qui se charge de convertir les nouvelles données entrantes vers un ensemble de données fini. Chaque nouvelle donnée peut donc être comparée à plusieurs motifs (un par classe) et un geste est reconnu dès lors que son score dépasse un seuil préalablement entrainé. Puis, un autre algorithme appelé SearchMax se charge de trouver un maximum local au sein d’une fenêtre glissant afin de préciser si oui ou non un geste a été reconnu. Cependant des conflits peuvent survenir et en ce sens un autre classifieur (c.-àd. C4.5) est chainé. Étant donné que l’algorithme de regroupement K-Means a besoin d’une valeur pour le nombre de regroupements à faire, nous introduisons également une technique simple d’optimisation à ce sujet. Cette partie d’optimisation se charge également de trouver la meilleure taille de fenêtre possible pour l’algorithme SearchMax. Afin de démontrer l’efficacité et la robustesse de notre algorithme, nous l’avons testé sur deux ensembles de données différents. Cependant, les résultats sur les ensembles de données testées n’étaient bons que lorsque les données d’entrainement étaient utilisées en tant que données de test. Cela peut être dû au fait que la méthode est dans un état de surapprentissage.
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Sahoo, Lagnajeet. "Hand Gesture Recognition System." Thesis, 2015. http://ethesis.nitrkl.ac.in/7739/1/602.pdf.
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Hand Gesture Recognition is a well-researched topic in the community of Machine Learning, Computer Graphics and Image Processing. The system which are based on Recognition technology follow mathematically rich and complicated algorithms whose main aim is to teach a computer different gestures. Because there are very large sets of gestures, the number of methodologies to identify the set of gestures is also large. In this thesis, I have concentrated on the gestures are based on hands. The thesis is divided into two sections namely: Static mode and Dynamic. The Static mode concentrates on gestures based on still images and Dynamic mode concentrates on gestures based on image sequence. As every hand gesture recognition system, the recognition paths has been divided into basically four parts for Static mode: Segmentation, Feature Extraction, Feature Selection and Classification. In the static mode the algorithm used are the Graph Cut algorithm, Bacterial foraging optimization algorithm, Support vector machine, binary tree color quantization algorithm, block-based discrete cosine transform. The Graph Cut algorithm uses the min-cut of a graph to separate the non-hand pixels from the hand pixels. The min-cut of the graph is found out by the max-flow algorithm. The binary tree color quantization algorithm is used to cluster the pixels into required number of clusters. The BFO algorithm is used to find the optimum value of parameter that are either required to be maximized or minimized. The BFO is an evolutionary algorithm which is a reflection of the swamping behavior of the E. Coli bacteria. The algorithm is a non-linear form of optimization and the convergence of the algorithm is faster than the other evolutionary algorithms. For Dynamic mode the path has been divided into four parts: Segmentation, Tracking, Feature Extraction, Vector Quantization and Classification. The Dynamic mode uses 150 frames of image data to trace the path of the hand and finds the most likely gesture. The hand isolation is done by use of Gaussian Mixture model. To make the system as fast as possible the tracking of hand was more preferred to be fast than accurate. So some amount of accuracy was sacrifice for the sake of performance. As the sequence of image is involved the Hidden Markov model was preferred method for the classification. The training of the HMM was done by the method described by Baum- Welch which is the maximization of the expected value of the parameters of the HMM. The training was followed by the testing where an image sequence of 150 frames was passed to the system. The Viterbi algorithm was used for the testing purposes. The Viterbi algorithm finds the most like sequence of states for which that particular sequence of observation is taken out.
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Pradhan, Lalit Mohan. "Gesture Based Character Recognition." Thesis, 2015. http://ethesis.nitrkl.ac.in/7806/1/2015_Gesture_Pradhan.pdf.
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Gesture is rudimentary movements of a human body part, which depicting the important movement of an individual. It is high significance for designing efficient human-computer interface. An proposed method for Recognition of character(English alphabets) from gesture i.e gesture is performed by the utilization of a pointer having color tip (is red, green, or blue). The color tip is segment from back ground by converting RGB to HSI color model. Motion of color tip is identified by optical flow method. During formation of multiple gesture the unwanted lines are removed by optical flow method. The movement of tip is recoded by Motion History Image(MHI) method. After getting the complete gesture, then each character is extracted from hand written image by using the connected component and the features are extracted of the correspond character. The recognition is performed by minimum distance classifier method (Modified Hausdorf Distance). An audio format of each character is store in data-set so that during the classification, the corresponding audio of character will play.
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Wu, Zong-Guei, and 吳宗桂. "Using KINECT Gesture Recognition for User Recognition." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/qau9uf.
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碩士國立虎尾科技大學
電機工程研究所
103
In recent years, the safe identification system used in intelligent environment has been attractive by people and more and more similarly systems were proposed. This paper presented a user identification based on posture and combined the skeleton data which gets from KINECT. It contains two types of features, including non-learning features and learning features of the learning methods. Based on human skeleton joints, there are three user features proposed by the author. The methods in sequence are “Adjacency Joint Distance”, “Confirm Skeleton Angle” and the last one is to combine of the above, and two learning features, “Gravity of Offset” (GLO), “Transfer Matrix of Offset” (TMLO). All of them were used in user identification system as the features. The paper are also using Support Vector Machine (SVM), Gaussian Mixture Model (GMM) and Principal Component Analysis (PCA) to develop user legality confirmed in SVM and develop user identity recognition in GMM and PCA. And propose three types of user recognition user recognition model, GMM-PCA、PCA learning-SVM、PCA learning-GMM, which trying to modify the original method of single model. Three types of non-learning features are separately training in SVM、GMM、PCA. And prefer to select features in better recognition rates. SVM and PCA we select “Adjacency Joint Distance” and GMM select combine features. Non-learning features was trained in GMM-PCA, total of score normalization GMM-PCA was decided to recognition result. The identification of action may change through the time and the habits of user, so it will affect the efficiency of each recognition process. To improve the situation, we add the learning method of machine and developed two learning algorithms. The paper is using Adjacency Joint Distance to train in PCA, and according to PCA learning methods to propose two types of learning offsets features, PCA-GLO and PCA-TMLO are training in SVM and GMM. PCA-GLO is learning 16 times training in SVM, the recognition rates was 94.3%. PCA-GLO is learning 16 times training in GMM, the recognition rates was 99.8%. And PCA-TMLO is learning 10 times training in SVM, the recognition rates was 98.9%. The experiment result, PCA-TMLO training in SVM was better than single SVM by more learning times, and learning times was better than PCA-GLO training in SVM. PCA-GLO training in GMM which recognition rates was better than single GMM. The experiment result, it proved the learning effect which the features was extracted in PCA learning methods.
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Chen, Chih-Yu, and 陳治宇. "Virtual Mouse：Vision-Based Gesture Recognition." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/74539959450046293234.
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碩士國立中山大學
資訊工程學系研究所
91
The thesis describes a method for human-computer interaction through vision-based gesture recognition and hand tracking, which consists of five phases: image grabbing, image segmentation, feature extraction, gesture recognition, and system mouse controlling. Unlike most of previous works, our method recognizes hand with just one camera and requires no color markers or mechanical gloves. The primary work of the thesis is improving the accuracy and speed of the gesture recognition. Further, the gesture commands will be used to replace the mouse interface on a standard personal computer to control application software in a more intuitive manner.
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TIWARI, MANU, and 馬麗麗. "Gesture Recognition in Shopping Scenario." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/q8edw2.
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碩士國立交通大學
電機資訊國際學程
107
Smart phones and Smart wristbands are being used for effective activity recognition for health management, personal identification, payment purposes etc. The shopping industry is not far behind in experimenting with these devices in order to make shopping experience better for customers, gaining more information on their behavior, benefiting businesses etc. This work here aims at recognizing activities performed during shopping using an inertial sensor. The study of segments generated and processed to develop a recognition model. The model is robust and light to be developed into a real-time application to recognize activities. The use of graphical features other than statistical features successfully added in increasing the accuracy. The sliding-overlapping window made the recognition model better.
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Mandal, Itishree, and Samiksha Ray. "Hand gesture based digit recognition." Thesis, 2014. http://ethesis.nitrkl.ac.in/6488/1/E-30.pdf.
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Recognition of static hand gestures in our daily plays an important role in human-computer interaction. Hand gesture recognition has been a challenging task now a days so a lot of research topic has been going on due to its increased demands in human computer interaction. Since Hand gestures have been the most natural communication medium among human being, so this facilitate efficient human computer interaction in many electronics gazettes . This has led us to take up this task of hand gesture recognition. In this project different hand gestures are recognized and no of fingers are counted. Recognition process involve steps like feature extraction, features reduction and classification. To make the recognition process robust against varying illumination we used lighting compensation method along with YCbCr model. Gabor filter has been used for feature extraction because of its special mathematical properties. Gabor based feature vectors have high dimension so in our project 15 local gabor filters are used instead of 40 Gabor filters. The objective in using fifteen Gabor filters is used to mitigate the complexity with improved accuracy. In this project the problem of high dimensionality of feature vector is being solved by using PCA. Using local Gabor filter helps in reduction of data redundancy as compared to that of 40 filters. Classification of the 5 different gestures is done with the use of one against all multiclass SVM which is also compared with Euclidean distance and cosine similarity while the former giving an accuracy of 90.86%.
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Chen, Jiunn-Yeuo, and 陳俊有. "Hand Gesture Commands for a PC Presentation:Hand Gesture Recognition andPointing Computation." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/64517050729864669654.
Full textAbstract:
碩士國立交通大學
資訊工程學系
85
In a PC presentation system, speakers must bow to control the mouse or keyboard key to move the screen upward, downward, leftward, rightward.This causes the time delay or interrupt of presentation. In this thesis, we want to remove this drawback. We use human gestures to replace the mouse function in the PC presentation system.We have eight hand gestures including up, down, left, right, zoom in, zoom out, hold and point. With two calibrated TV cameras, we capture the hand images by frame grabber and do image processing.At last, we send the hand gesture recognition result to the PC presentation system via a RS232 network.
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Chen, Feng-Sheng, and 陳豐生. "Gesture Recognition Using Hidden Markov Models." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/39515573353219025950.
Full textAbstract:
碩士國立清華大學
電機工程學系
87
In this thesis, we introduce a hand gesture recognition system to recognize continuous gesture in simple background. The system consists of three modules: feature extraction, hidden Markov model (HMM) training, and gesture recognition using the HMMs. First, we apply the motion information to extract the hand-shape and apply the scale and rotation-invariant Fourier descriptor to characterize hand figures. Then we combine Fourier descriptor and motion information of input image sequence as our feature vector. After having extracted the feature vector, we first train our system using HMM approach and then use the trained HMMs to recognize the input gesture. In training phase, we apply hidden Markov Model to describe the gestures properties (generating the initial state probability distribution, the state transition probability distribution and the observation probability distribution) for each gesture. To recognize gesture, the gesture to be recognized in separately scored against different HMMs. The model with the highest score is selected as the recognized gesture. Our system consists of 20 different hand gestures. The experimental results show that the average recognition rate is 88.5%.
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Chen, Kuan-Wei, and 陳冠緯. "Gesture recognition of smart mobile device." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/62wfvm.
Full textAbstract:
碩士樹德科技大學
資訊工程系碩士班
104
With technology advancements, today’s smart mobile devices are moving towards increasingly higher performance specifications. This thesis puts the neural network training that could only be run on home computers or higher level devices in the past to run on today’s smart mobile devices. For the purpose of this thesis, a system was designed on an Android smart mobile device. Acceleration values were obtained using the existing gravitational acceleration sensor in the device and finite state capture and then went through average filtering and normalization before being sent to a backpropagation neural network for neural network training. Upon the completion of the training, recall data in the neural network was obtained likewise using the acceleration sensor in the smart mobile device for gesture recognition. The results of this experiment show that using a smart mobile device for real-time acceleration value capture is a feasible approach and it is possible to use the device as usual while the neural network training is running thanks to Android’s service lifecycle feature. The smart mobile device used was a Sony Z3 and the gestures to be recognized were handwritten Arabic numbers 0~9. Observations were made using different quantities of hidden layer neurons and training samples. Observed results reveal that the quantity of neurons does not have any significant effect on the accuracy of gesture recognition while the impact of training sample size is more evident, i.e. the greater the training sample size, the higher the accuracy and the longer the training time. It is therefore VI necessary to consider how to balance between training time and accuracy. The shortest training time was 86 minutes when using 100 training samples and 50 neurons. The longest was 432 minutes when using 200 training samples and 60 neurons. The training sample size for numbers 0~9 were 10, 15 and 20. When the number of neurons in the same hidden layer was 60, the average accuracy of gesture recognition reached up to 87%, 87.5% and 89.5%. When the number of neurons in the hidden layer was 50, 60, 70 and 80, the average accuracy of gesture recognition reached up to 85%, 87%, 87% and 86%. Therefore, integrating data capture, neural network training and gesture recognition in one smart mobile device is a feasible approach. It is recommended to choose data with more fluctuating acceleration values as training samples, which can increase the success rate of gesture recognition. It is also recommended to choose a higher performance smart mobile device, which can reduce training time
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Chao-Hui, Huang, and 黃朝暉. "Silhouette-Based Hand Gesture Recognition System." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/93267423724812911907.
Full textAbstract:
碩士中華大學
資訊工程學系碩士班
89
Let computer science taking over some jobs of human is a dream. However, Duplicating of human intuition is very difficult. In this paper, we try to duplicate the vision of human intuition, and present that by human hand gesture recognition with low computation requirement. Usually, the human hand gesture recognition system requires either high cost of computation, or special auxiliary devices. Due to this, a faster and convenient method becomes necessary. For the sake of real-time implementation, we developed two main algorithms: Curve Detection Algorithm (CDA) and Peak Detection Algorithm (PDA), where CDA extracts the feature from the silhouette pattern of image and PDA extracts specific patterns in silhouette image which implied peak information. Basically, we have developed a new method for hand gesture extraction with lower cost of computation and less devices requirement. Those method is provided for the method of hand gesture extraction base on light spot which is in existence. Using CDA and PDA, we may able to extract the feature of hand gesture as same as there are light spots which be wore on the hand and treat the fingers tips and valley as light spot.
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AlSharif, Mohammed H. "Hand Gesture Recognition Using Ultrasonic Waves." Thesis, 2016. http://hdl.handle.net/10754/609434.
Full textAbstract:
Gesturing is a natural way of communication between people and is used in our
everyday conversations. Hand gesture recognition systems are used in many applications in a wide variety of fields, such as mobile phone applications, smart TVs, video gaming, etc. With the advances in human-computer interaction technology, gesture recognition is becoming an active research area. There are two types of devices to detect gestures; contact based devices and contactless devices. Using ultrasonic waves for determining gestures is one of the ways that is employed in contactless devices. Hand gesture recognition utilizing ultrasonic waves will be the focus of this thesis
work. This thesis presents a new method for detecting and classifying a predefined set of hand gestures using a single ultrasonic transmitter and a single ultrasonic receiver. This method uses a linear frequency modulated ultrasonic signal. The ultrasonic signal is designed to meet the project requirements such as the update rate, the range of detection, etc. Also, it needs to overcome hardware limitations such as the limited output power, transmitter, and receiver bandwidth, etc. The method can be adapted to other hardware setups. Gestures are identified based on two main features; range estimation of the moving hand and received signal strength (RSS). These two factors are estimated using two simple methods; channel impulse response (CIR) and cross correlation (CC) of the reflected ultrasonic signal from the gesturing hand. A customized simple hardware setup was used to classify a set of hand gestures with high accuracy. The detection and classification were done using methods of low computational cost. This makes the proposed method to have a great potential for the implementation in many devices including laptops and mobile phones. The predefined set of gestures can be used for many control applications.
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Brás, André Filipe Pereira. "Gesture recognition using deep neural networks." Master's thesis, 2017. http://hdl.handle.net/10316/83023.
Full textAbstract:
Dissertação de Mestrado Integrado em Engenharia Mecânica apresentada à Faculdade de Ciências e TecnologiaEsta dissertação teve como principal objetivo o desenvolvimento de um método para realizar segmentação e reconhecimento de gestos. A pesquisa foi motivada pela importância do reconhecimento de ações e gestos humanos em aplicações do mundo real, como a Interação Homem-Máquina e a compreensão de linguagem gestual. Além disso, pensa-se que o estado da arte atual pode ser melhorado, já que esta é uma área de pesquisa em desenvolvimento contínuo, com novos métodos e ideias surgindo frequentemente.A segmentação dos gestos envolveu um conjunto de características artesanais extraídas dos dados 3D do esqueleto, as quais são adequadas para representar cada frame de qualquer sequência de vídeo, e uma Rede Neuronal Artificial para distinguir momentos de descanso de períodos de atividade. Para o reconhecimento de gestos, foram desenvolvidos 3 modelos diferentes. O reconhecimento usando as características artesanais e uma janela deslizante, que junta informação ao longo da dimensão temporal, foi a primeira abordagem. Além disso, a combinação de várias janelas deslizantes com o intuito de obter a influência de diferentes escalas temporais também foi experimentada. Por último, todas as características artesanais foram descartadas e uma Rede Neuronal Convolucional foi usada com o objetivo de extrair automaticamente as características e as representações mais importantes a partir de imagens.Todos os métodos foram testados no conjunto de dados do concurso 2014 Looking At People e o melhor alcançou um índice de Jaccard de 0.71. O desempenho é quase equivalente ao de algumas técnicas do estado da arte.
This dissertation had as the main goal the development of a method to perform gesture segmentation and recognition. The research was motivated by the significance of human action and gesture recognition in real world applications, such as Human-Machine Interaction (HMI) and sign language understanding. Furthermore, it is thought that the current state of the art can be improved, since this is an area of research in continuous developing, with new methods and ideas emerging frequently.The gesture segmentation involved a set of handcrafted features extracted from 3D skeleton data, which are suited to characterize each frame of any video sequence, and an Artificial Neural Network (ANN) to distinguish resting moments from periods of activity. For the gesture recognition, 3 different models were developed. The recognition using the handcrafted features and a sliding window, which gathers information along the time dimension, was the first approach. Furthermore, the combination of several sliding windows in order to reach the influence of different temporal scales was also experienced. Lastly, all the handcrafted features were discarded and a Convolutional Neural Network (CNN) was used with the aim to automatically extract the most important features and representations from images.All the methods were tested in 2014 Looking At People Challenge’s data set and the best one achieved a Jaccard index of 0.71. The performance is almost on pair with that of some of the state of the art techniques.
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TSAI, HU-CHUNG, and 蔡鵠仲. "Research on Gesture Recognition Controlled Quadcopter." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/24984313688431062793.
Full textAbstract:
碩士國立高雄海洋科技大學
輪機工程研究所
104
In this thesis, control methods analysis and design for quadcopter are considered. A human–machine interface of gesture recognition is developed to control the quadcopter. The main system architecture includes the quadcopter, the synchronous attitude simulation system, the proportional-integral-derivative (PID) controller, and a human-machine interface of gesture recognition. The quadcopter mechanism is designed in size 330mm*330mm with X-shaped configuration of the motor structure. Synchronous attitude simulation system is mainly used to obtain the quadcopter’s flight attitude via the corrected parameter values of acceleration and gyro sensors. The control of motor speed and achievement of stable quadcopter are based on the flight attitude and PID controller design. Human-machine interface of gesture recognition is sensing the position of the hand gesture. The interactive graphical interface is used to detect the hand position and status and control the flight of quadcopter. Finally, some experimental results validate the control approach of quadcopter on the proposed gesture recognition.