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Topcu, Hasan Huseyin. "Human Body Part Detection And Multi-human Tracking Insurveillance Videos." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614308/index.pdf.
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With the recent developments in Computer Vision and Pattern Recognition, surveillance applications are equipped with the capabilities of event/activity understanding and interpretation which usually require recognizing humans in real world scenes. Real world scenes such
as airports, streets and train stations are complex because they involve many people, complicated occlusions and cluttered backgrounds. Although complex real world scenes exist, human detectors have the capability to locate pedestrians accurately even in complex scenes
and visual trackers have the capability to track targets in cluttered environments. The integration of visual object detection and tracking, which are the fundamental features of available
surveillance applications, is one of the solutions for multi-human tracking problem in crowded scenes which is studied in this thesis.
In this thesis, human body part detectors, which are capable of detecting human heads and human upper body parts, are trained with Support Vector Machines (SVM) by using Histogram of Oriented Gradients (HOG), which is one of the state-of-the-art descriptor for human detection. The training process is elaborated by investigating the effects of the parameters of
the HOG descriptor. The human heads and upper body parts are searched in the region of interests (ROI) computed by detecting motion. In addition, these human body part detectors are integrated with a multi-human tracker which solves the data association problem with the
Multi Scan Markov Chain Monte Carlo Data Association (MCMCDA) algorithm. Associated measurements of human upper body part locations are used for state correction for each track.
State estimation is done through Kalman Filter. The performance of detectors are evaluated using MIT Pedestrian dataset and INRIA Human dataset.
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Wren, Christopher R. (Christopher Richard). "Pfinder : real-time tracking of the human body." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10652.
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Bao, Guanqun. "On Simultaneous Localization and Mapping inside the Human Body (Body-SLAM)." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-dissertations/206.
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Wireless capsule endoscopy (WCE) offers a patient-friendly, non-invasive and painless investigation of the entire small intestine, where other conventional wired endoscopic instruments can barely reach. As a critical component of the capsule endoscopic examination, physicians need to know the precise position of the endoscopic capsule in order to identify the position of intestinal disease after it is detected by the video source. To define the position of the endoscopic capsule, we need to have a map of inside the human body. However, since the shape of the small intestine is extremely complex and the RF signal propagates differently in the non-homogeneous body tissues, accurate mapping and localization inside small intestine is very challenging. In this dissertation, we present an in-body simultaneous localization and mapping technique (Body-SLAM) to enhance the positioning accuracy of the WCE inside the small intestine and reconstruct the trajectory the capsule has traveled. In this way, the positions of the intestinal diseases can be accurately located on the map of inside human body, therefore, facilitates the following up therapeutic operations. The proposed approach takes advantage of data fusion from two sources that come with the WCE: image sequences captured by the WCE's embedded camera and the RF signal emitted by the capsule. This approach estimates the speed and orientation of the endoscopic capsule by analyzing displacements of feature points between consecutive images. Then, it integrates this motion information with the RF measurements by employing a Kalman filter to smooth the localization results and generate the route that the WCE has traveled. The performance of the proposed motion tracking algorithm is validated using empirical data from the patients and this motion model is later imported into a virtual testbed to test the performance of the alternative Body-SLAM algorithms. Experimental results show that the proposed Body-SLAM technique is able to provide accurate tracking of the WCE with average error of less than 2.3cm.
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Zhang, Qing. "HIGH QUALITY HUMAN 3D BODY MODELING, TRACKING AND APPLICATION." UKnowledge, 2015. http://uknowledge.uky.edu/cs_etds/39.
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Geometric reconstruction of dynamic objects is a fundamental task of computer vision and graphics, and modeling human body of high fidelity is considered to be a core of this problem. Traditional human shape and motion capture techniques require an array of surrounding cameras or subjects wear reflective markers, resulting in a limitation of working space and portability. In this dissertation, a complete process is designed from geometric modeling detailed 3D human full body and capturing shape dynamics over time using a flexible setup to guiding clothes/person re-targeting with such data-driven models. As the mechanical movement of human body can be considered as an articulate motion, which is easy to guide the skin animation but has difficulties in the reverse process to find parameters from images without manual intervention, we present a novel parametric model, GMM-BlendSCAPE, jointly taking both linear skinning model and the prior art of BlendSCAPE (Blend Shape Completion and Animation for PEople) into consideration and develop a Gaussian Mixture Model (GMM) to infer both body shape and pose from incomplete observations. We show the increased accuracy of joints and skin surface estimation using our model compared to the skeleton based motion tracking. To model the detailed body, we start with capturing high-quality partial 3D scans by using a single-view commercial depth camera. Based on GMM-BlendSCAPE, we can then reconstruct multiple complete static models of large pose difference via our novel non-rigid registration algorithm. With vertex correspondences established, these models can be further converted into a personalized drivable template and used for robust pose tracking in a similar GMM framework. Moreover, we design a general purpose real-time non-rigid deformation algorithm to accelerate this registration. Last but not least, we demonstrate a novel virtual clothes try-on application based on our personalized model utilizing both image and depth cues to synthesize and re-target clothes for single-view videos of different people.
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Renna, I. "Upper body tracking and Gesture recognition for Human-Machine Interaction." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2012. http://tel.archives-ouvertes.fr/tel-00717443.
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Les robots sont des agents artificiels qui peuvent agir dans le monde des humains grâce aux capacités de perception. Dans un contexte d'interaction homme-robot, les humains et les robots partagent le même espace de communication. En effet, les robots compagnons sont censés communiquer avec les humains d'une manière naturelle et intuitive: l'une des façons les plus naturelles est basée sur les gestes et les mouvements réactifs du corps. Pour rendre cette interaction la plus conviviale possible, un robot compagnon doit, donc, être doté d'une ou plusieurs capacités lui permettant de percevoir, de reconnaître et de réagir aux gestes humains. Cette thèse a été focalisée sur la conception et le développement d'un système de reconnaissance gestuelle dans un contexte d'interaction homme-robot. Ce système comprend un algorithme de suivi permettant de connaître la position du corps lors des mouvements et un module de niveau supérieur qui reconnaît les gestes effectués par des utilisateurs humains. De nouvelles contributions ont été apportées dans les deux sujets. Tout d'abord, une nouvelle approche est proposée pour le suivi visuel des membres du haut du corps. L'analyse du mouvement du corps humain est difficile, en raison du nombre important de degrés de liberté de l'objet articulé qui modélise la partie supérieure du corps. Pour contourner la complexité de calcul, chaque membre est suivi avec un filtre particulaire à recuit simulé et les différents filtres interagissent grâce à la propagation de croyance. Le corps humain en 3D est ainsi qualifié comme un modèle graphique dans lequel les relations entre les parties du corps sont représentées par des distributions de probabilité conditionnelles. Le problème d'estimation de la pose est donc formulé comme une inférence probabiliste sur un modèle graphique, où les variables aléatoires correspondent aux paramètres des membres individuels (position et orientation) et les messages de propagation de croyance assurent la cohérence entre les membres. Deuxièmement, nous proposons un cadre permettant la détection et la reconnaissance des gestes emblématiques. La question la plus difficile dans la reconnaissance des gestes est de trouver de bonnes caractéristiques avec un pouvoir discriminant (faire la distinction entre différents gestes) et une bonne robustesse à la variabilité intrinsèque des gestes (le contexte dans lequel les gestes sont exprimés, la morphologie de la personne, le point de vue, etc). Dans ce travail, nous proposons un nouveau modèle de normalisation de la cinématique du bras reflétant à la fois l'activité musculaire et l'apparence du bras quand un geste est effectué. Les signaux obtenus sont d'abord segmentés et ensuite analysés par deux techniques d'apprentissage : les chaînes de Markov cachées et les Support Vector Machine. Les deux méthodes sont comparées dans une tâche de reconnaissance de 5 classes de gestes emblématiques. Les deux systèmes présentent de bonnes performances avec une base de données de formation minimaliste quels que soient l'anthropométrie, le sexe, l'âge ou la pose de l'acteur par rapport au système de détection. Le travail présenté ici a été réalisé dans le cadre d'une thèse de doctorat en co-tutelle entre l'Université "Pierre et Marie Curie" (ISIR laboratoire, Paris) et l'Université de Gênes (IIT - Tera département) et a été labelisée par l'Université Franco-Italienne.
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Renna, Ilaria. "Upper body tracking and Gesture recognition for Human-Machine Interaction." Paris 6, 2012. http://www.theses.fr/2012PA066119.
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Les robots sont des agents artificiels qui peuvent agir dans le monde des humains grâce aux capacités de perception. Dans un contexte d’interaction homme-robot, les humains et les robots partagent le même espace de communication. En effet, les robots compagnons sont censés communiquer avec les humains d’une manière naturelle et intuitive: l’une des façons les plus naturelles est basée sur les gestes et les mouvements réactifs du corps. Pour rendre cette interaction la plus conviviale possible, un robot compagnon doit, donc, être doté d’une ou plusieurs capacités lui permettant de percevoir, de reconnaître et de réagir aux gestes humains. Cette thèse a été focalisée sur la conception et le développement d’un système de reconnaissance gestuelle dans un contexte d’interaction homme-robot. Ce système comprend un algorithme de suivi permettant de connaître la position du corps lors des mouvements et un module de niveau supérieur qui reconnaît les gestes effectués par des utilisateurs humains. De nouvelles contributions ont été apportées dans les deux sujets. Tout d’abord, une nouvelle approche est proposée pour le suivi visuel des membres du haut du corps. L’analyse du mouvement du corps humain est difficile, en raison du nombre important de degrés de liberté de l’objet articulé qui modélise la partie supérieure du corps. Pour contourner la complexité de calcul, chaque membre est suivi avec un filtre particulaire à recuit simulé et les différents filtres interagissent grâce à la propagation de croyance. Le corps humain en 3D est ainsi qualifié comme un modèle graphique dans lequel les relations entre les parties du corps sont représentées par des distributions de probabilité conditionnelles. Le problème d’estimation de la pose est donc formulé comme une inférence probabiliste sur un modèle graphique, où les variables aléatoires correspondent aux paramètres des membres individuels (position et orientation) et les messages de propagation de croyance assurent la cohérence entre les membres. Deuxièmement, nous proposons un cadre permettant la détection et la reconnais- sance des gestes emblématiques. La question la plus difficile dans la reconnaissance des gestes est de trouver de bonnes caractéristiques avec un pouvoir discriminant (faire la distinction entre différents gestes) et une bonne robustesse à la variabilité intrinsèque des gestes (le contexte dans lequel les gestes sont exprimés, la morpholo- gie de la personne, le point de vue, etc). Dans ce travail, nous proposons un nouveau modèle de normalisation de la cinématique du bras reflétant à la fois l’activité mus- culaire et l’apparence du bras quand un geste est effectué. Les signaux obtenus sont d’abord segmentés et ensuite analysés par deux techniques d’apprentissage : les chaînes de Markov cachées et les Support Vector Machine. Les deux méthodes sont comparées dans une tâche de reconnaissance de 5 classes de gestes emblématiques. Les deux systèmes présentent de bonnes performances avec une base de données de formation minimaliste quels que soient l’anthropométrie, le sexe, l’âge ou la pose de l’acteur par rapport au système de détection. Le travail présenté ici a été réalisé dans le cadre d’une thèse de doctorat en co-tutelle entre l’Université “Pierre et Marie Curie” (ISIR laboratoire, Paris) et l’Université de Gênes (IIT - Tera département) et a été labelisée par l’Université Franco-Italienne<br>Robots are artificial agents that can act in humans’ world thanks to perception, action and reasoning capacities. In particular, robots companion are designed to share with humans the same physical and communication spaces in performing daily life collaborative tasks and aids. In such a context, interactions between humans and robots are expected to be as natural and as intuitive as possible. One of the most natural ways is based on gestures and reactive body motions. To make this friendly interaction possible, a robot companion has to be endowed with one or more capabilities allowing him to perceive, to recognize and to react to human gestures. This PhD thesis has been focused on the design and the development of a gesture recognition system that can be exploited in a human-robot interaction context. This system includes (1) a limbs-tracking algorithm that determines human body position during movements and (2) a higher-level module that recognizes gestures performed by human users. New contributions were made in both topics. First, a new approach is proposed for visual tracking of upper-body limbs. Analysing human body motion is challenging, due to the important number of degrees of freedom of the articulated object modelling the upper body. To circumvent the computational complexity, each limb is tracked with an Annealed Particle Filter and the different filters interact through Belief Propagation. 3D human body is described as a graphical model in which the relationships between the body parts are represented by conditional probability distributions. Pose estimation problem is thus formulated as a probabilistic inference over a graphical model, where the random variables correspond to the individual limb parameters (position and orientation) and Belief Propagation messages ensure coherence between limbs. Secondly, we propose a framework allowing emblematic gestures detection and recognition. The most challenging issue in gesture recognition is to find good features with a discriminant power (to distinguish between different gestures) and a good robustness to intrinsic gestures variability (the context in which gestures are expressed, the morphology of the person, the point of view, etc. ). In this work, we propose a new arm's kinematics normalization scheme reflecting both the muscular activity and arm's appearance when a gesture is performed. The obtained signals are first segmented and then analysed by two machine learning techniques: Hidden Markov Models and Support Vector Machines. The two methods are compared in a 5 classes emblematic gestures recognition task. Both systems show good performances with a minimalistic training database regardless to performer's anthropometry, gender, age or pose with regard to the sensing system. The work presented here has been done within the framework of a PhD thesis in joint supervision between the “Pierre et Marie Curie” University (ISIR laboratory, Paris) and the University of Genova (IIT--Tera department) and was labelled by the French-Italian University
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Lu, Yao. "Human body tracking and pose estimation from monocular image sequences." Thesis, Curtin University, 2013. http://hdl.handle.net/20.500.11937/1665.
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This thesis describes a bottom-up approach to estimating human pose over time based on monocular views with no restriction on human activities,Three approaches are proposed to address the weaknesses of existing approaches, including building a specific appearance model using clustering,utilising both the generic and specific appearance models in the estimation, and building an uncontaminated appearance model by removing backgroundpixels from the training samples. Experimental results show that the proposed system outperforms existing system significantly.
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Azhar, Faisal. "Marker-less human body part detection, labelling and tracking for human activity recognition." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/69575/.
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This thesis focuses on the development of a real-time and cost effective marker-less computer vision method for significant body point or part detection (i.e., the head, arm, shoulder, knee, and feet), labelling and tracking, and its application to activity recognition. This work comprises of three parts: significantbody point detection and labelling, significant body point tracking, and activity recognition. Implicit body models are proposed based on human anthropometry, kinesiology, and human vision inspired criteria to detect and label significant body points. The key idea of the proposed method is to fit the knowledge from the implicit body models rather than fitting the predefined models in order to detect and label significant body points. The advantages of this method are that it does not require manual annotation, an explicit fitting procedure, and a training (learning) phase, and it is applicable to humans with different anthropometric proportions. The experimental results show that the proposed method robustly detects and labels significant body points in various activities of two different (low and high) resolution data sets. Furthermore, a Particle Filter with memory and feedback is proposed that combines temporal information of the previous observation and estimation with feedback to track significant body points in occlusion. In addition, in order to overcome the problem presented by the most occluded body part, i.e., the arm, a Motion Flow method is proposed. This method considers the human arm as a pendulum attached to the shoulder joint and defines conjectures to track the arm since it is the most occluded body part. The former method is invoked as default and the latter is used as per a user's choice. The experimental results show that the two proposed methods, i.e., Particle Filter and Motion Flow methods, robustly track significant body points in various activities of the above-mentioned two data sets and also enhance the performance of significant body point detection. A hierarchical relaxed partitioning system is then proposed that employs features extracted from the significant body points for activity recognition when multiple overlaps exist in the feature space. The working principle of the proposed method is based on the relaxed hierarchy (postpone uncertain decisions) and hierarchical strategy (group similar or confusing classes) while partitioning each class at different levels of the hierarchy. The advantages of the proposed method lie in its real-time speed, ease of implementation and extension, and non-intensive training. The experimental results show that it acquires valuable features and outperforms relevant state-of-the-art methods while comparable to other methods, i.e., the holistic and local feature approaches. In this context, the contribution of this thesis is three-fold: Pioneering a method for automated human body part detection and labelling. Developing methods for tracking human body parts in occlusion. Designing a method for robust and efficient human action recognition.
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Abedan, Kondori Farid. "Bring Your Body into Action : Body Gesture Detection, Tracking, and Analysis for Natural Interaction." Doctoral thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-88508.
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Due to the large influx of computers in our daily lives, human-computer interaction has become crucially important. For a long time, focusing on what users need has been critical for designing interaction methods. However, new perspective tends to extend this attitude to encompass how human desires, interests, and ambitions can be met and supported. This implies that the way we interact with computers should be revisited. Centralizing human values rather than user needs is of the utmost importance for providing new interaction techniques. These values drive our decisions and actions, and are essential to what makes us human. This motivated us to introduce new interaction methods that will support human values, particularly human well-being. The aim of this thesis is to design new interaction methods that will empower human to have a healthy, intuitive, and pleasurable interaction with tomorrow’s digital world. In order to achieve this aim, this research is concerned with developing theories and techniques for exploring interaction methods beyond keyboard and mouse, utilizing human body. Therefore, this thesis addresses a very fundamental problem, human motion analysis. Technical contributions of this thesis introduce computer vision-based, marker-less systems to estimate and analyze body motion. The main focus of this research work is on head and hand motion analysis due to the fact that they are the most frequently used body parts for interacting with computers. This thesis gives an insight into the technical challenges and provides new perspectives and robust techniques for solving the problem.
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Fang, Bing. "A Framework for Human Body Tracking Using an Agent-based Architecture." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77135.
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The purpose of this dissertation is to present our agent-based human tracking framework, and to evaluate the results of our work in light of the previous research in the same field.
Our agent-based approach departs from a process-centric model where the agents are bound to specific processes, and introduces a novel model by which agents are bound to the objects or sub-objects being recognized or tracked. The hierarchical agent-based model allows the system to handle a variety of cases, such as single people or multiple people in front of single or stereo cameras. We employ the job-market model for agents' communication. In this dissertation, we will present several experiments in detail, which demonstrate the effectiveness of the agent-based tracking system.
Per our research, the agents are designed to be autonomous, self-aware entities that are capable of communicating with other agents to perform tracking within agent coalitions. Each agent with high-level abstracted knowledge seeks evidence for its existence from the low-level features (e.g. motion vector fields, color blobs) and its peers (other agents representing body-parts with which it is compatible). The power of the agent-based approach is its flexibility by which the domain information may be encoded within each agent to produce an overall tracking solution.<br>Ph. D.
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Mikić, Ivana. "Human body model acquisition and tracking using multi-camera voxel data /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2002. http://wwwlib.umi.com/cr/ucsd/fullcit?p3036991.
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Wong, Shu-fai, and 黃樹輝. "The Application of human body tracking for the development of a visualinterface." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30103009.
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Jolly, James, Joe Bishop, and Emilio Nanni. "Tracking the Human Body Via a Wireless Network of Pyroelectric Sensor Arrays." International Foundation for Telemetering, 2008. http://hdl.handle.net/10150/606242.
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ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, California<br>This paper describes the design and construction of a low-cost wireless sensor network (WSN) intended to track a human body walking upright through its physical topology. The network consists of arrays of pyroelectric infrared (PIR) sensors that can detect a moving body up to five meters away within a semicircular field of view. Data is gathered from these arrays and transmitted to a central processor that triangulates the body's position. Important characteristics of both the PIR sensors and the network's asynchronous nature are elaborated upon to illustrate how they affect the interpretation of the data.
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D'Apuzzo, Nicola D'Apuzzo Nicola D'Apuzzo Nicola. "Surface measurement and tracking of human body parts from multi station video sequences /." Zürich : Institut für Geodäsie und Photogrammetrie, 2003. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=15271.
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Aparicio, Conrado. "Implementation of a quaternion-based Kalman filter for human body motion tracking using MARG sensors." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Sep%5FAparicio.pdf.
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Elanattil, Shafeeq. "Non-rigid 3D reconstruction of the human body in motion." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/205095/1/Shafeeq_Elanattil_Thesis.pdf.
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This thesis addresses the challenging problem of three-dimensional (3-D) reconstruction of a fast-moving human, using a single moving camera which captures both depth and colour information (RGB-D). Our objective is to find solutions to the challenges arising from the high camera motion and articulated human motions. We have developed an effective system which uses the camera pose, skeleton detection, and multi-scale information, to produce a robust reconstruction framework for 3-D modelling of fast-moving humans. The outcome of the research is useful for several applications of human performance capture systems in sports, the arts, and animation industries.
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Sinav, Alper. "Analysis and modeling of the virtual human interface for the MARG body tracking system using quaternions." Thesis, Monterey, California. Naval Postgraduate School, 2002. http://hdl.handle.net/10945/6018.
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Approved for public release; distribution unlimited<br>This thesis done in cooperation with the MOVES Institute<br>Mathematicians have used quaternions for about a hundred years. Today they are an important part of computer graphics and simulation systems. This thesis takes an analytical approach to quaternions by using them in the construction of a virtual human for sourceless Magnetic Accelerometer Rate Sensor (MARG) body tracking system. Virtual citizens will be a reflection of our personalities in cyberspace. Prophecies say they may take control in the virtual world and govern themselves too. One of the objectives of this thesis is to design a seamless and realistic humanoid from laser scan data clouds. This humanoid will be compatible with motion capture systems and networked virtual environments. Second objective of this thesis is to search for the answers related to the optimal real-time representation of an articulated virtual human, maintaining a high level of visual fidelity within networked cyberspace. While visual detail and fidelity have been and will continue to be a major ongoing interest within the computer graphics community, the idea of sourceless body tracking is still in its early stages. MARG body tracking is one of the successful approaches to body tracking systems. This thesis proposes a networked quaternion based real-time virtual human interface for the MARG body tracking system.
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Jurzykowski, Michal. "Eye Tracking in User Interfaces." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236489.
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Tato diplomová práce byla vytvořena během studijního pobytu na Uviversity of Estern Finland, Joensuu, Finsko. Tato diplomová práce se zabývá využitím technologie sledování pohledu neboli také sledování pohybu očí (Eye-Tracking) pro interakci člověk-počítač (Human-Computer Interaction (HCI)). Navržený a realizovaný systém mapuje pozici bodu pohledu/zájmu (the point of gaze), která odpovídá souřadnicím v souřadnicovém systému kamery scény do souřadnicového systému displeje. Zároveň tento systém kompenzuje pohyby uživatele a tím odstraňuje jeden z hlavních problémů využití sledování pohledu v HCI. Toho je dosaženo díky stanovení transformace mezi projektivním prostorem scény a projektivním prostorem displeje. Za použití význačných bodů (interesting points), které jsou nalezeny a popsány pomocí metody SURF, vyhledání a spárování korespondujících bodů a vypočítání homografie. Systém byl testován s využitím testovacích bodů, které byly rozložené po celé ploše displeje.
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Twyman, Nathan W. "Automated Human Screening for Detecting Concealed Knowledge." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/222874.
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Screening individuals for concealed knowledge has traditionally been the purview of professional interrogators investigating a crime. But the ability to detect when a person is hiding important information would be of high value to many other fields and functions. This dissertation proposes design principles for and reports on an implementation and empirical evaluation of a non-invasive, automated system for human screening. The screening system design (termed an automated screening kiosk or ASK) is patterned after a standard interviewing method called the Concealed Information Test (CIT), which is built on theories explaining psychophysiological and behavioral effects of human orienting and defensive responses. As part of testing the ASK proof of concept, I propose and empirically examine alternative indicators of concealed knowledge in a CIT. Specifically, I propose kinesic rigidity as a viable cue, propose and instantiate an automated method for capturing rigidity, and test its viability using a traditional CIT experiment. I also examine oculomotor behavior using a mock security screening experiment using an ASK system design. Participants in this second experiment packed a fake improvised explosive device (IED) in a bag and were screened by an ASK system. Results indicate that the ASK design, if implemented within a highly controlled framework such as the CIT, has potential to overcome barriers to more widespread application of concealed knowledge testing in government and business settings.
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Fallah, Haghmohammadi Hamidreza. "Fever Detection for Dynamic Human Environment Using Sensor Fusion." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37332.
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The objective of this thesis is to present an algorithm for processing infrared images and accomplishing automatic detection and path tracking of moving subjects with fever. The detection is based on two main features: the distinction between the geometry of a human face and other objects in the field of view of the camera and the temperature of the radiating object. These features are used for tracking the identified person with fever. The position of camera with respect to direction of motion the walkers appeared to be critical in this process. Infrared thermography is a remote sensing technique used to measure temperatures based on emitted infrared radiation. This application may be used for fever screening in major public places such as airports and hospitals. For this study, we first look at human body and objects in a line of view with different temperatures that would be higher than the normal human body temperature (37.8C at morning and 38.3C at evening). As a part of the experimental study, two humans with different body temperatures walking a path were subjected to automatic fever detection applied for tracking the detected human with fever. The algorithm consists of image processing to threshold objects based on the temperature and template matching used for fever detection in a dynamic human environment.
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Miezal, Markus [Verfasser]. "Models, methods and error source investigation for real-time Kalman filter based inertial human body tracking / Markus Miezal." München : Verlag Dr. Hut, 2021. http://d-nb.info/1232847631/34.
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Shaban, Heba Ahmed. "A Novel Highly Accurate Wireless Wearable Human Locomotion Tracking and Gait Analysis System via UWB Radios." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/27562.
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Gait analysis is the systematic study of human walking. Clinical gait analysis is the process by which quantitative information is collected for the assessment and decision-making of any gait disorder. Although observational gait analysis is the therapistâ s primary clinical tool for describing the quality of a patientâ s walking pattern, it can be very unreliable. Modern gait analysis is facilitated through the use of specialized equipment. Currently, accurate gait analysis requires dedicated laboratories with complex settings and highly skilled operators. Wearable locomotion tracking systems are available, but they are not sufficiently accurate for clinical gait analysis. At the same time, wireless healthcare is evolving. Particularly, ultra wideband (UWB) is a promising technology that has the potential for accurate ranging and positioning in dense multi-path environments. Moreover, impulse-radio UWB (IR-UWB) is suitable for low-power and low-cost implementation, which makes it an attractive candidate for wearable, low-cost, and battery-powered health monitoring systems. The goal of this research is to propose and investigate a full-body wireless wearable human locomotion tracking system using UWB radios. Ultimately, the proposed system should be capable of distinguishing between normal and abnormal gait, making it suitable for accurate clinical gait analysis.<br>Ph. D.
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Zhu, Youding. "Model-Based Human Pose Estimation with Spatio-Temporal Inferencing." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1242752509.
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Basharat, Arslan. "MODELING SCENES AND HUMAN ACTIVITIES IN VIDEOS." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3830.
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In this dissertation, we address the problem of understanding human activities in videos by developing a two-pronged approach: coarse level modeling of scene activities and fine level modeling of individual activities. At the coarse level, where the resolution of the video is low, we rely on person tracks. At the fine level, richer features are available to identify different parts of the human body, therefore we rely on the body joint tracks. There are three main goals of this dissertation: (1) identify unusual activities at the coarse level, (2) recognize different activities at the fine level, and (3) predict the behavior for synthesizing and tracking activities at the fine level. The first goal is addressed by modeling activities at the coarse level through two novel and complementing approaches. The first approach learns the behavior of individuals by capturing the patterns of motion and size of objects in a compact model. Probability density function (pdf) at each pixel is modeled as a multivariate Gaussian Mixture Model (GMM), which is learnt using unsupervised expectation maximization (EM). In contrast, the second approach learns the interaction of object pairs concurrently present in the scene. This can be useful in detecting more complex activities than those modeled by the first approach. We use a 14-dimensional Kernel Density Estimation (KDE) that captures motion and size of concurrently tracked objects. The proposed models have been successfully used to automatically detect activities like unusual person drop-off and pickup, jaywalking, etc. The second and third goals of modeling human activities at the fine level are addressed by employing concepts from theory of chaos and non-linear dynamical systems. We show that the proposed model is useful for recognition and prediction of the underlying dynamics of human activities. We treat the trajectories of human body joints as the observed time series generated from an underlying dynamical system. The observed data is used to reconstruct a phase (or state) space of appropriate dimension by employing the delay-embedding technique. This transformation is performed without assuming an exact model of the underlying dynamics and provides a characteristic representation that will prove to be vital for recognition and prediction tasks. For recognition, properties of phase space are captured in terms of dynamical and metric invariants, which include the Lyapunov exponent, correlation integral, and correlation dimension. A composite feature vector containing these invariants represents the action and will be used for classification. For prediction, kernel regression is used in the phase space to compute predictions with a specified initial condition. This approach has the advantage of modeling dynamics without making any assumptions about the exact form (polynomial, radial basis, etc.) of the mapping function. We demonstrate the utility of these predictions for human activity synthesis and tracking.<br>Ph.D.<br>School of Electrical Engineering and Computer Science<br>Engineering and Computer Science<br>Computer Science PhD
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Rius, Ferrer Ignasi. "Motion Priors for Efficient Bayesian Tracking In Iluman Sequence Evaluation." Doctoral thesis, Universitat Autònoma de Barcelona, 2010. http://hdl.handle.net/10803/5798.
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La reconstrucció del moviment huma mitjançant l'analisi visual és una area de recerca de la visió per computador plena de reptes amb moltes aplicacions potencials. Els enfocs de seguiment basat en models, i en particular els fltres de partícules, formulen el problema com una tasca d'inferencia Bayesiana l'objectiu de la qual és estimar seqüencialment la distribució sobre els parametres d'un model del cos huma al llarg del temps. Aquests enfocs depenen en gran mesuta d'emprar bons models dinamics i d'observació per tal de predir i actualitzar les confguracions del cos huma en base a mesures extretes de les dades d'imatge. No obstant, resulta molt difícil dissenyar models d'observació, i en especial pel cas de seguiment a partir d'una sola vista, que siguin capaços d'extreure informació útil de les seqüencies d'imatges de manera robusta. Per tant, per tal de superar aquestes limitacions és necessari emprar un fort coneixement a priori sobre el moviment huma i guiar així l'exploració de l'espai d'estats. <br/>El treball presentat en aquesta Tesis esta enfocat a recuperar els parametres de moviment 3D d'un model del cos huma a partir de mesures incompletes i sorolloses d'una seqüencia d'imatges monocular. Aquestes mesures consisteixen en les posicions 2D d'un conjunt redult d'articulacions en el pla d'imatge. Amb aquesta fnalitat, proposem un nou model de moviment huma específc per cada acció, que és entrenat a partir de bases de dades de captures de moviment que contenen varies execucions d'una acció en particular, i que és utilitzat com a coneixement a priori en un esquema de fltratge de partícules. <br/>Les postures del cos es representen emprant un model articulat simple i compacte que fa ús dels cosinus directors per tal de representar la direcció de les parts del cos en l'espai Cartesia 3D. Llavors, donada una acció, s'aplica l'Analisis de Components Principals (PCA) sobre les dades d'entrenament per tal d'aplicar reducció de dimensionalitat sobre les dades d'entrada altament correlacionades. Previament al pas d'entrenament del model d'acció, les seqüencies de moviment d'entrada són sincronitzades mitjançant un nou algoritme d'adaptació dens basat en Programació Dinamica. L'algoritme sincronitza totes les seqüencies de moviment d'una mateixa classe d'acció i és capa¡ de trobar una solució óptima en temps real. <br/>Aleshores, s'apren un model d'acció probabilístic a partir dels exemples de movi¬ment sincronitzats que captura la variabilitat i l'evolució temporal del moviment del cos sencer durant una acció concreta. En particular, per cada acció, els parametres apresos són: una varietat representativa de l'acció que consisteix en l'execució mitjana de la mateixa, la desviació estandard de l'execució mitjana, els vectors de direcció mitjans de cada subseqüencia de moviment d'una llargada donada i l'error esperat en un instant de temps donat. <br/>A continuació, s'utilitza el model específc per cada acció com a coneixement a priori sobre moviment huma que millora l'efciencia i robustesa de tot l'enfoc de seguiment basat en fltratge de partícules. En primer lloc, el model dinamic guia les partícules segons situacions similars apreses previament. A continuació, es restringeix l'espai d'estats per tal que tan sols les postures humanes més factibles siguin acceptades com a solucions valides a cada instant de temps. En conseqüencia, l'espai d'estats és explorat de manera més efcient ja que el conjunt de partícules cobreix les postures del cos més probables. <br/>Finalment, es duen a terme experiments emprant seqüencies de test de varies bases de dades. Els resultats assenyalen que el nostre esquema de seguiment és capa d'estimar la confguració 3D aproximada d'un model de cos sencer, a partir tan sols de les posicions 2D d'un conjunt redult d'articulacions. També s'inclouen proves separades sobre el metode de sincronització de seqüencies i de la tecnica de comparació probabilística de les subseqüencies de moviment.<br>Recovering human motion by visual analysis is a challenging computer vision research area with a lot of potential applications. Model based tracking approaches, and in particular particle flters, formulate the problem as a Bayesian inference task whose aim is to sequentially estimate the distribution of the parameters of a human body model over time. These approaches strongly rely on good dynamical and observation models to predict and update confgurations of the human body according to mea surements from the image data. However, it is very difcult to design observation models which extract useful and reliable information from image sequences robustly. This results specially challenging in monocular tracking given that only one viewpoint from the scene is available. Therefore, to overcome these limitations strong motion priors are needed to guide the exploration of the state space. <br/>The work presented in this Thesis is aimed to retrieve the 3D motion parameters of a human body model from incomplete and noisy measurements of a monocular image sequence. These measurements consist of the 2D positions of a reduced set of joints in the image plane. Towards this end, we present a novel action specifc model of human motion which is trained from several databases of real motion captured performances of an action, and is used as a priori knowledge within a particle fltering scheme. <br/>Body postures are represented by means of a simple and compact stick fgure model which uses direction cosines to represent the direction of body limbs in the 3D Cartesian space. Then, for a given action, Principal Component Analysis is applied to the training data to perform dimensionality reduction over the highly correlated input data. Before the learning stage of the action model, the input motion performances are synchronized by means of a novel dense matching algorithm based on Dynamic Programming. The algorithm synchronizes all the motion sequences of the same action class, fnding an optimal solution in real time. <br/>Then, a probabilistic action model is learnt, based on the synchronized motion examples, which captures the variability and temporal evolution of full body motion within a specifc action. In particular, for each action, the parameters learnt are: a representative manifold for the action consisting of its mean performance, the stan dard deviation from the mean performance, the mean observed direction vectors from each motion subsequence of a given length and the expected error at a given time instant. <br/>Subsequently, the action specifc model is used as a priori knowledge on human motion which improves the efciency and robustness of the overall particle fltering tracking framework. First, the dynamic model guides the particles according to similar situations previously learnt. Then, the state space is constrained so only feasible human postures are accepted as valid solutions at each time step. As a result, the state space is explored more efciently as the particle set covers the most probable body postures. <br/>Finally, experiments are carried out using test sequences from several motion databases. Results point out that our tracker scheme is able to estimate the rough 3D confguration of a full body model providing only the 2D positions of a reduced set of joints. Separate tests on the sequence synchronization method and the subsequence probabilistic matching technique are also provided. <br/>Keywords: Human Motion Modeling; Particle fltering; Monocular Full Body 3D Tracking. <br/>Topics: Image Processing; Computer Vision; Scene Understanding; Machine Intelligence; Machine Vision Applications; Video-Sequence Evaluation
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Rudol, Piotr. "Increasing Autonomy of Unmanned Aircraft Systems Through the Use of Imaging Sensors." Licentiate thesis, Linköpings universitet, UASTECH – Teknologier för autonoma obemannade flygande farkoster, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71295.
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The range of missions performed by Unmanned Aircraft Systems (UAS) has been steadily growing in the past decades thanks to continued development in several disciplines. The goal of increasing the autonomy of UAS's is widening the range of tasks which can be carried out without, or with minimal, external help. This thesis presents methods for increasing specific aspects of autonomy of UAS's operating both in outdoor and indoor environments where cameras are used as the primary sensors. First, a method for fusing color and thermal images for object detection, geolocation and tracking for UAS's operating primarily outdoors is presented. Specifically, a method for building saliency maps where human body locations are marked as points of interest is described. Such maps can be used in emergency situations to increase the situational awareness of first responders or a robotic system itself. Additionally, the same method is applied to the problem of vehicle tracking. A generated stream of geographical locations of tracked vehicles increases situational awareness by allowing for qualitative reasoning about, for example, vehicles overtaking, entering or leaving crossings. Second, two approaches to the UAS indoor localization problem in the absence of GPS-based positioning are presented. Both use cameras as the main sensors and enable autonomous indoor ight and navigation. The first approach takes advantage of cooperation with a ground robot to provide a UAS with its localization information. The second approach uses marker-based visual pose estimation where all computations are done onboard a small-scale aircraft which additionally increases its autonomy by not relying on external computational power.
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Rosten, Edward James. "High performance rigid body tracking." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.614011.
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Hansen, Hedvik Louise. "Human Tracking System." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-26769.
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This thesis is motivated by Statoil's wish to localize people operating inside an onshore or offshore production facility. There are especially two benefits of such systems. By monitoring an operator's exposure to noise, vibration and harmful gases, short and long term damages can be avoided. In addition, a localization system would provide employees with a higher degree of safety in case of emergency. The aim of this thesis is to develop a prototype system for localizing people indoors as well as outdoors with use of an inertial measurement unit. As a consequence, challenges such as drift and signal noise, as well as magnetic interference are directed. Since the IMU is subject to drift, investigations on different sensor configurations must be conducted. Collected sensor data from the hip-mounted IMU are used for step detection, step length estimation and heading determination. By combining the results from these three approaches, a positioning estimate for the human operator is calculated using dead reckoning. Dead reckoning is a positioning method for determining an objects location based on a former position, velocity and direction.Experiments indicated satisfying performance by both the implemented step detection algorithm and the step length estimation model. However, the heading determination is subject to magnetic interferences when the sensor is applied in an industrial area. In an indoors environment are position error 2-4 meters after 22 meters walking. Performance in an outdoors environment are significantly better, with a position error of 10 meter after 400 meters. Integrating the suggested solution with another positioning system will increase system performance indoors and thus fulfill the aim of this thesis.
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Kabelac, Zachary (Zachary E. ). "3D tracking via body radio reflections." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91834.
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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 65-66).<br>This thesis presents WiTrack, a system that tracks the 3D motion of a user from the radio signals reflected off her body. It works even if the person is occluded from the WiTrack device or in a different room. WiTrack does not require the user to carry any wireless device, yet its accuracy exceeds current RF localization systems, which require the user to hold a transceiver. Empirical measurements with a WiTrack prototype show that, on average, it localizes the center of a human body to within a median of 10 to 13 cm in the x and y dimensions, and 21 cm in the z dimension. It also provides coarse tracking of body parts, identifying the direction of a pointing hand with a median of 11.2°. WiTrack bridges a gap between RF-based localization systems which locate a user through walls and occlusions, and human-computer interaction systems like Kinect, which can track a user without instrumenting her body, but require the user to stay within the direct line of sight of the device.<br>by Zachary Kabelac.<br>M. Eng.
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Xu, Qingguo. "3D Body Tracking using Deep Learning." UKnowledge, 2017. http://uknowledge.uky.edu/cs_etds/59.
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This thesis introduces a 3D body tracking system based on neutral networks and 3D geometry, which can robustly estimate body poses and accurate body joints. This system takes RGB-D data as input. Body poses and joints are firstly extracted from color image using deep learning approach. The estimated joints and skeletons are further translated to 3D space by using camera calibration information. This system is running at the rate of 3 4 frames per second. It can be used to any RGB-D sensors, such as Kinect, Intel RealSense [14] or any customized system with color depth calibrated. Comparing to the sate-of-art 3D body tracking system, this system is more robust, and can get much more accurate joints locations, which will benefits projects require precise joints, such as virtual try-on, body measure, real-time avatar driven.
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Sundaravadivel, Prabha. "Application-Specific Things Architectures for IoT-Based Smart Healthcare Solutions." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1157532/.
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Human body is a complex system organized at different levels such as cells, tissues and organs, which contributes to 11 important organ systems. The functional efficiency of this complex system is evaluated as health. Traditional healthcare is unable to accommodate everyone's need due to the ever-increasing population and medical costs. With advancements in technology and medical research, traditional healthcare applications are shaping into smart healthcare solutions. Smart healthcare helps in continuously monitoring our body parameters, which helps in keeping people health-aware. It provides the ability for remote assistance, which helps in utilizing the available resources to maximum potential. The backbone of smart healthcare solutions is Internet of Things (IoT) which increases the computing capacity of the real-world components by using cloud-based solutions. The basic elements of these IoT based smart healthcare solutions are called "things." Things are simple sensors or actuators, which have the capacity to wirelessly connect with each other and to the internet. The research for this dissertation aims in developing architectures for these things, focusing on IoT-based smart healthcare solutions. The core for this dissertation is to contribute to the research in smart healthcare by identifying applications which can be monitored remotely. For this, application-specific thing architectures were proposed based on monitoring a specific body parameter; monitoring physical health for family and friends; and optimizing the power budget of IoT body sensor network using human body communications. The experimental results show promising scope towards improving the quality of life, through needle-less and cost-effective smart healthcare solutions.
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Montgomery, Eric W. "Design and Implementation of Real-Time Software for Sourceless Full Body-Tracking using Small Inertial/Magnetic Sensors." Miami University / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=miami1051192415.
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Pan, Wenbo. "Real-time human face tracking." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0018/MQ55535.pdf.
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Wan, Mingchao. "Form and Human Body." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/50489.
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Architectural form offers an expression and an observer receives an impression. This interaction exists at both intellectual (mind) and physical (body) levels. Through designing a sculpture pavilion in a forest, this thesis explores different means of empathetic expression in modern architectural form.<br>Master of Architecture
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GRUZMAN, MAURICIO. "TARGET TRACKING SYSTEM MOUNTED IN A MOVING BODY." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=17533@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>Neste trabalho estuda-se um sistema de acompanhamento de alvos, também
conhecido como sistema de rastreamento de alvos, do tipo pan-tilt atuado por
motores de corrente contínua e fixo em um corpo em movimento. Para tanto é
montada uma bancada de testes e implementa-se um programa de simulação. A
modelagem para o programa é feita no domínio do tempo, permitindo a utilização
de equações bastante complexas para representar o sistema, o que não é possível
quando se utiliza modelos no domínio da freqüência. Apesar de se modelar o
sistema com corpos rígidos, flexibilidades e amortecimentos estruturais devido aos
redutores de velocidade são considerados. Erros nos sensores, folgas nos
redutores, atritos seco e viscoso, limites de saturação para as correntes e tensões
nas armaduras dos motores também são considerados. Um método para a inclusão
dos atrasos de tempo para atualização dos sinais de controle e dados obtidos pelos
sensores durante a integração numérica das equações de movimento é apresentado.
Para controlar o sistema utilizam-se controladores que não requerem o modelo
matemático da planta, tanto na bancada de testes como no programa de simulação.
Três tipos diferentes de arquitetura de controle são propostas, chamadas neste
trabalho de tipo 1, tipo 2 e tipo 3. A complexidade delas aumenta à medida que
mais sensores estão disponíveis no sistema. A arquitetura do tipo 1 destina-se a
sistemas onde se possui apenas sensores que fornecem os erros angulares de
azimute e elevação do alvo. Se, além deste sensor, também houver sensores para
medir as posições angulares relativas entre os elos do mecanismo usa-se a
arquitetura do tipo 2. Se houver, ainda, sensores de velocidades angulares inerciais
pode-se utilizar a arquitetura do tipo 3. Por fim são apresentados resultados de
experimentos e simulações onde se compara o desempenho do sistema com cada
tipo de arquitetura de controle.<br>A study on a pan-tilt type target tracking system actuated by permanent
magnet DC motors and assembled in a moving body is presented in this work. To
achieve such objective, an experimental test bed is constructed and a simulation
program is implemented. The mechanical model is derived and simulated in time
domain. This approach allows using accurate non-linear equations to represent
system behavior, otherwise infeasible in frequency domain. Although the system
is modeled with rigid bodies, flexibility and structural damping due to the
gearboxes are considered. Sensor errors, backlash in the gearboxes, dry and
viscous friction, saturation limits for armature current and tension of the motors
are also considered. A method to include the time delays for the control signal
updates, as well as time delays due to sensor dynamic response, during the
numerical integration of the equations of motion, is presented. Controllers that
require no mathematical model of the plant are employed in the experimental test
bed and in the simulation program. Three different control architectures are
proposed, called in this work type 1, type 2 and type 3. Their complexity increases
depending on the number of available sensors. The type 1 is applied to systems
with only one sensor that provides the targets angular azimuth and elevation
errors. If, besides this sensor, sensors to measure the relative angular positions
between the mechanism links are available type 2 architecture is used. In addition,
if sensors to measure inertial angular speeds are also available, type 3 architecture
can be used. Finally, experimental and numerical results, comparing system
performance with each control architecture are presented.
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Pantazis, Ioannis. "Tracking human walking using MARG sensors." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Jun%5FPantazis.pdf.
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Thesis (M.S. in Electrical Engineering and M.S. in Systems Engeineering)--Naval Postgraduate School, June 2005.<br>Thesis Advisor(s): Xiaoping Yun. Includes bibliographical references (p. 93-95). Also available online.
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Karlsson, Daniel. "Human Motion Tracking Using 3D Camera." Thesis, Linköping University, Department of Electrical Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54426.
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<p>The interest in video surveillance has increased in recent years. Cameras are now installed in e.g. stores, arenas and prisons. The video data is analyzed to detect abnormal or undesirable events such as thefts, fights and escapes. At the Informatics Unit at the division of Information Systems, FOI in Linköping, algorithms are developed for automatic detection and tracking of humans in video data. This thesis deals with the target tracking problem when a 3D camera is used. A 3D camera creates images whose pixels represent the ranges to the scene. In recent years, new camera systems have emerged where the range images are delivered at up to video rate (30 Hz). One goal of the thesis is to determine how range data affects the frequency with which the measurement update part of the tracking algorithm must be performed. Performance of the 2D tracker and the 3D tracker are evaluated with both simulated data and measured data from a 3D camera. It is concluded that the errors in the estimated image coordinates are independent of whether range data is available or not. The small angle and the relatively large distance to the target explains the good performance of the 2D tracker. The 3D tracker however shows superior tracking ability (much smaller tracking error) if the comparison is made in the world coordinates.</p>
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Laberge, Dominic. "Visual tracking for human-computer interaction." Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/26504.
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The purpose of this master's thesis project is to design, implement and evaluate vision-based user interfaces for use in the context of virtual environments. Three interfaces are treated. The first one is a 4 degrees of freedom (DOF) mouse that can track the position and 1 DOF or rotation (roll) of a user's hand.
The second one is a 6 DOF mouse that can track both the position and the orientation of a user's hand in 3D space.
Finally the third one is a laser pointing interface used to track the laser spot of a standard laser pointer, in order to interact with a large screen display.
The two latter interfaces use an auto-calibrated approach based on planar homography, that distinguish them from the standard computer-vision based approach which requires a previous step of calibration.
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Luo, Tao, and 羅濤. "Human visual tracking in surveillance video." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206727.
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Visual surveillance in dynamic scenes, especially for human activities, is one of the current challenging research topics in computer vision. It is a key technology to fight against terrorism and crime to ensure public safety. The motivation of this thesis is to design an efficient human visual tracking system for video surveillance deployed in complex environments.
In video surveillance, detection of moving objects is the first step to analyze the video streams. And motion segmentation is one of popular approaches to do it. In this thesis, we propose a motion segmentation method to overcome the problem of motion blurring.
The task of human tracking is key to the effective use of more advanced technologies, like activity recognition and behavior understanding. However, human tracking routines often fail either due to human's arbitrary movements or occlusions by other objects. To overcome human's arbitrary movement, we propose a new Silhouette Chain Shift model for human detection and tracking. To track human under occlusions, firstly each frame is represented by a scene energy which consists of all the moving objects. Then the process of tracking is converted to a process of minimizing the proposed scene energy.
Findings from the thesis contribute to improve the performance of human visual tracking system and therefore improve security in areas under surveillance.<br>published_or_final_version<br>Computer Science<br>Doctoral<br>Doctor of Philosophy
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Nguyen, Nhat-Tan. "Human motion tracking from movie sequences." Thesis, Université Laval, 2011. http://www.theses.ulaval.ca/2011/28170/28170.pdf.
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Zhang, Xiao. "Data-driven human body morphing." Thesis, Texas A&M University, 2005. http://hdl.handle.net/1969.1/2655.
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This thesis presents an efficient and biologically informed 3D human body morphing technique through data-driven alteration of standardized 3D models. The anthropometric data is derived from a large empirical database and processed using principal component analysis (PCA). Although techniques using PCA are relatively commonplace in computer graphics, they are mainly used for scientific visualizations and animation. Here we focus on uncovering the underlying mathematical structure of anthropometric data and using it to build an intuitive interface that allows the interactive manipulation of body shape within the normal range of human variation. We achieve weight/gender based body morphing by using PCA. First we calculate the principal vector space of the original data. The data then are transformed into a new orthogonal multidimensional space. Next, we reduce the dimension of the data by only keeping the components of the most significant principal vectors. We then fit a curve through the original data points and are able to generate a new human body shape by inversely transforming the data from principal vector space back to the original measuring data space. Finally, we sort the original data by the body weight, calculating males and females separately. This enables us to use weight and gender as two intuitive controls for body morphing. The Deformer program is implemented using the programming language C++ with OPENGL and FLTK API. 3D and human body models are created using Alias MayaTm.
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Hakl, Henry. "Computer-controlled human body coordination." Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/49756.
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Thesis (MSc) -- University of Stellenbosch, 2003.<br>ENGLISH ABSTRACT: A need for intelligent robotic machines is identified. Research and experiments have focussed
on stable, or relatively stable, dynamic simulated systems to demonstrate the feasibility of
embedding advanced AI into dynamic physical systems. This thesis presents an attempt to
scale the techniques to a dynamically highly unstable system - the coordination of movements
in a humanoid model. Environmental simulation, articulated systems and artificial intelligence
methods are identified as three essential layers for a complete and unified approach to embedding
AI into robotic machinery. The history of the physics subsystem for this project is discussed,
leading to the adoption of the Open Dynamics Engine as the physics simulator of choice. An
approach to articulated systems is presented along with the EBNF of a hierarchical articulated
system that was used to describe the model. A revised form of evolution is presented and
justified. An AI model that makes use of this new evolutionary paradigm is introduced. A
variety of AI variants are defined and simulated. The results of these simulations are presented
and analysed. Based on these results recommendations for future work are made.<br>AFRIKAANSE OPSOMMING: Die beheer van dinamiese masjiene, soos intelligente robotte, is tans beperk tot fisies stabilie
- of relatief stabiele - sisteme. In hierdie tesis word die tegnieke van kunsmatige intelligensie
(KI) toegepas op die kontrole en beheer van 'n dinamies hoogs onstabiele sisteem: 'n Humanoïede
model. Fisiese simulasie, geartikuleerde sisteme en kunmatige intelligensie metodes
word geïdentifiseer as drie noodsaaklike vereistes vir 'n volledige en eenvormige benadering tot
KI beheer in robotte. Die implementasie van 'n fisiese simulator word beskryf, en 'n motivering
vir die gebruik van die sogenaamde "Open Dynamics Engine" as fisiese simulator word gegee.
'n Benadering tot geartikuleerde sisteme word beskryf, tesame met die EBNF van 'n hierargiese
geartikuleerde sisteem wat gebruik is om die model te beskryf. 'n Nuwe interpretasie vir evolusie
word voorgestel, wat die basis vorm van 'n KI model wat in die tesis gebruik word. 'n
Verskeidenheid van KI variasies word gedefineer en gesimuleer, en die resultate word beskryf
en ontleed. Voorstelle vir verdere navorsing word gemaak.
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Mufti, H. (Haseeb). "Human body communication performance simulations." Master's thesis, University of Oulu, 2016. http://urn.fi/URN:NBN:fi:oulu-201606092482.
Full textAbstract:
Human Body Communication (HBC) is a novel communication method between devices which use human body as a transmission medium. This idea is mostly based on the concept of wireless biomedical monitoring system. The on-body sensor nodes can monitor vital signs of a human body and use the body as a transmission medium. This technology is convenient for long durations of clinical monitoring with the option of more mobility and freedom for the user.
In this thesis, IEEE 802.15.6-2012 physical (PHY) layer for the HBC was simulated. Simulation model is following the standard’s requirements and processes. The human body was taken as a transmission medium and simulations, which follow the HBC standard, have been carried out. For the purpose of simulations, MATLAB is used as a platform to test and run the simulations.
The constants and variables used in the simulations are taken from the IEEE 802.15 working group for wireless personal area networks (WPANs). The transmitter model and the receiver model have been taken from the standard, with changes done in it for performing the simulations on the PHY layer only. The simulations were done keeping in mind the dielectric properties of the outer layer of a human body, i.e., the dielectric values for human skin are noted and their corresponding values were used in the mathematical calculations.
The work done here presents a transmitter and receiver architecture for the human body communication. The minimum data rate being 164 kbps and the transmitter being designed around the 21 MHz center frequency has achieved some outputs which are worth looking. The channel models used in this simulator are HBC channel and AWGN (additive white Gaussian noise) channel. It was observed that when signal was passed through AWGN channel, noise was added uniformly over the signal, while in the HBC channel signal strength is directly proportional to the transceiver ground sizes. In conclusion, the size of the ground terminals plays a critical role for the signal quality in the HBC simulator.
The results in this thesis show that pathloss has certain linearity with the distance. The pathloss is calculated for different parts of the body with higher loss for structure with higher amount of bone, and vice versa. It is observed that in the HBC channel there are four factors with high impact on the system. These are the distances between the transceiver in air and on body while the other two are the sizes of the transceiver grounds. The size of the transmitter ground has been deemed very significant for the HBC from the simulations results. The four factors show high impact on the HBC channel. The signal strength is highly effected with the change in these four characteristics. From the simulation results it is evident that the HBC channel show a 15 to 20 dB deviation when compared to AWGN channel. The Eb⁄N0 for BER level at 10^(-3) for AWGN channel is 10 to 11 dB while for HBC it is around 27 dB showing a significant difference in the results.
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Williams, Daniel Patrick 1964. "Body composition, blood pressure and their tracking in children and adolescents." Thesis, The University of Arizona, 1989. http://hdl.handle.net/10150/277028.
Full textAbstract:
Blood pressure (BP) measurement reliability, year-to-year BP tracking, distinguishing characteristics of upper quintile (UQ) vs lower four quintiles' (LQ) systolic BP (SBP) tracking and the relationships of fat distribution and body composition to SBP were examined in 57 youth. Subjects were measured on two occasions approximately one year apart. Longitudinal measures included auscultatory BPs, height, weight, body circumferences, skeletal widths, bioelectrical impedance and skinfolds. Inter-trial reliability of right/left arm averaged BP (RLBP) exceeded that of either limb alone; tracking magnitude was likewise greater with RLBP. Greater total body mass and fatness as well as larger anthropometric dimensions distinguished UQ from LQ SBP trackers. Fat distribution and SBP were not consistently associated with each other across study years. Irrespective of gender differences, fatness and fat free mass per unit height2 were independently related to within year SBP, yet only initial fatness was independently predictive of future SBP.
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Mathi, Krishna Chaithanya. "Augment HoloLens’ Body Recognition and Tracking Capabilities Using Kinect." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1484670493776915.
Full text
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Craig, Pippa. "Which body size? : a cross-cultural study of body composition and body perception." Phd thesis, Faculty of Medicine, 1999. http://hdl.handle.net/2123/12824.
Full text
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Kucuk, Can. "3d Marker Tracking For Human Gait Analysis." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/2/12606941/index.pdf.
Full textAbstract:
This thesis focuses on 3D marker tracking for human gait analysis. In KISS Gait Analysis System at METU, a subject'<br>s gait is recorded with 6 cameras while 13 reflective markers are attached at appropriate locations on his/her legs and feet. These images are processed to extract 2 dimensional (2D) coordinates of the markers in each camera. The 3 dimensional (3D) coordinates of the markers are obtained by processing the 2D coordinates of the markers with linearization and calibration algorithms. Then 3D trajectories of the markers are formed using the 3D coordinates of the markers. In this study, software which takes the 2D coordinates of markers in each camera and processes them to form the 3D trajectories of the markers is developed. Kalman Filter is used in formation of 3D trajectories. The results are found to be satisfactory.
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Drewes, Heiko. "Eye Gaze Tracking for Human Computer Interaction." Diss., lmu, 2010. http://nbn-resolving.de/urn:nbn:de:bvb:19-115914.
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Yang, Lin. "3D Sensing and Tracking of Human Gait." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32540.
Full textAbstract:
Motion capture technology has been applied in many fields such as animation, medicine, military, etc. since it was first proposed in the 1970s. Based on the principles applied, motion capture technology is generally classified into six categories: 1) Optical; 2) Inertial; 3) Magnetic; 4) Mechanical; 5) Acoustic and 6) Markerless. Different from the other five kinds of motion capture technologies which try to track path of specific points with different equipment, markerless systems recognize human or non-human body's motion with vision-based technology which focuses on analyzing and processing the captured images for motion capture. The user doed not need to wear any equipment and is free to do any action in an extensible measurement area while a markerless motion capture system is working. Though this kind of system is considered as the preferred solution for motion capture, the difficulty for realizing an effective and high accuracy markerless system is much higher than the other technologies mentioned, which makes markerless motion capture development a popular research direction. Microsoft Kinect sensor has attracted lots of attention since the launch of its first version with its depth sensing feature which gives the sensor the ability to do motion capture without any extra devices. Recently, Microsoft released a new version of Kinect sensor with improved hardware and and targeted at the consumer market. However, to the best of our knowlege, the accuracy assessment of the sensor remains to be answered since it was released. In this thesis, we measure the depth accuracy of the newly released Kinect v2 depth sensor from different aspects and propose a trilateration method to improve the depth accuracy with multiple Kinects simultaneously. Based on the trilateration method, a low-cost, no wearable equipment requirement and easy setup human gait tracking system is realized.
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Gao, Hongzhi. "Real Time Human Tracking in Unconstrained Environments." Thesis, University of Canterbury. Computer Science and Software Engineering, 2011. http://hdl.handle.net/10092/5683.
Full textAbstract:
The tabu search particle filter is proposed in this research based on the integration of the modified tabu search metaheuristic optimization and the genetic particle filter. Experiments with this algorithm in real time human tracking applications in unconstrained environments show that it is more robust, accurate and faster than a number of other existing metaheuristic filters, including the evolution particle filter, particle swarm filter, simulated annealing filter, path relink filter and scatter search filter. Quantitative evaluation illustrates that even with only ten particles in the system, the proposed tabu search particle filter has a success rate of 93.85% whereas the success rate of other metaheuristic filters ranged from 68.46% to 17.69% under the same conditions. The accuracy of the proposed algorithm (with ten particles in the tracking system) is 2.69 pixels on average, which is over 3.85 times better than the second best metaheuristic filters in accuracy and 18.13 times better than the average accuracy of all other filters. The proposed algorithm is also the fastest among all metaheuristic filters that have been tested. It achieves approximately 50 frames per second, which is 1.5 times faster than the second fastest algorithm and nineteen times faster than the average speed of all other metaheuristic filters.
Furthermore, a unique colour sequence model is developed in this research based on a degenerated form of the hidden Markov model. Quantitative evaluations based on rigid object matching experiments illustrate that the successful matching rate is 5.73 times better than the widely used colour histogram. In terms of speed, the proposed algorithm achieves twice the successful matching rate in about three quarters of the processing time consumed by the colour histogram model.
Overall, these results suggest that the two proposed algorithms would be useful in many applications due to their efficiently, accuracy and ability to robustly track people and coloured objects.