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Journal articles on the topic 'Vision Based Gesture Recognition'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Lou, Xinyue. "Vision-based Hand Gesture Recognition Technology." Applied and Computational Engineering 141, no. 1 (2025): 54–59. https://doi.org/10.54254/2755-2721/2025.21696.

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Human-computer interaction has a wide range of application prospects in many fields such as medicine, entertainment, industry and education. Gesture recognition is one of the most important technologies for gesture interaction between humans and robots, and visual gesture recognition increases the user's comfort and freedom compared with data glove recognition. This paper summarizes the general process of visual gesture recognition based on the literature, including three steps: pre-processing, feature extraction, and gesture classification. It also defines static and dynamic gestures and make

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P, Hrishikesh, Akshay V, Anugraha K, T. R. Hari Subramaniam, and Jyothisha J. Nair. "Vision Based Gesture Recognition." Procedia Computer Science 235 (2024): 303–15. http://dx.doi.org/10.1016/j.procs.2024.04.031.

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Jasim, Mahmood, Tao Zhang, and Md Hasanuzzaman. "A Real-Time Computer Vision-Based Static and Dynamic Hand Gesture Recognition System." International Journal of Image and Graphics 14, no. 01n02 (2014): 1450006. http://dx.doi.org/10.1142/s0219467814500065.

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This paper presents a novel method for computer vision-based static and dynamic hand gesture recognition. Haar-like feature-based cascaded classifier is used for hand area segmentation. Static hand gestures are recognized using linear discriminant analysis (LDA) and local binary pattern (LBP)-based feature extraction methods. Static hand gestures are classified using nearest neighbor (NN) algorithm. Dynamic hand gestures are recognized using the novel text-based principal directional features (PDFs), which are generated from the segmented image sequences. Longest common subsequence (LCS) algor

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Gupta, Himanshu, Aniruddh Ramjiwal, and Jasmin T. Jose. "Vision Based Approach to Sign Language Recognition." International Journal of Advances in Applied Sciences 7, no. 2 (2018): 156. http://dx.doi.org/10.11591/ijaas.v7.i2.pp156-161.

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We propose an algorithm for automatically recognizing some certain amount of gestures from hand movements to help deaf and dumb and hard hearing people. Hand gesture recognition is quite a challenging problem in its form. We have considered a fixed set of manual commands and a specific environment, and develop a effective, procedure for gesture recognition. Our approach contains steps for segmenting the hand region, locating the fingers, and finally classifying the gesture which in general terms means detecting, tracking and recognising. The algorithm is non-changing to rotations, translations

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Himanshu, Gupta, Ramjiwal Aniruddh, and T. Jose Jasmin. "Vision Based Approach to Sign Language Recognition." International Journal of Advances in Applied Sciences (IJAAS) 7, no. 2 (2018): 156–61. https://doi.org/10.11591/ijaas.v7.i2.pp156-161.

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We propose an algorithm for automatically recognizing some certain amount of gestures from hand movements to help deaf and dumb and hard hearing people. Hand gesture recognition is quite a challenging problem in its form. We have considered a fixed set of manual commands and a specific environment, and develop a effective, procedure for gesture recognition. Our approach contains steps for segmenting the hand region, locating the fingers, and finally classifying the gesture which in general terms means detecting, tracking and recognising. The algorithm is non-changing to rotations, translations

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RAUTARAY, SIDDHARTH S., and ANUPAM AGRAWAL. "VISION-BASED APPLICATION-ADAPTIVE HAND GESTURE RECOGNITION SYSTEM." International Journal of Information Acquisition 09, no. 01 (2013): 1350007. http://dx.doi.org/10.1142/s0219878913500071.

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With the increasing role of computing devices, facilitating natural human computer interaction (HCI) will have a positive impact on their usage and acceptance as a whole. For long time, research on HCI has been restricted to techniques based on the use of keyboard, mouse, etc. Recently, this paradigm has changed. Techniques such as vision, sound, speech recognition allow for much richer form of interaction between the user and machine. The emphasis is to provide a natural form of interface for interaction. Gestures are one of the natural forms of interaction between humans. As gesture commands

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Yang, Lewei. "Real-time gesture-based control of UAVs using multimodal fusion of FMCW radar and vision." Journal of Physics: Conference Series 2664, no. 1 (2023): 012002. http://dx.doi.org/10.1088/1742-6596/2664/1/012002.

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Abstract Gesture-based control has gained prominence as an intuitive and natural means of interaction with unmanned aerial vehicles (UAVs). This paper presents a real-time gesture-based control system for UAVs that leverages the multimodal fusion of Frequency Modulated Continuous Wave (FMCW) radar and vision sensors, aiming to enhance user experience through precise and responsive UAV control via hand gestures. The research focuses on developing an effective fusion framework that combines the complementary advantages of FMCW radar and vision sensors. FMCW radar provides robust range and veloci

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Yu, Hengcheng, and Zhengyu Chen. "Research on contactless control of elevator based on machine vision." Highlights in Science, Engineering and Technology 7 (August 3, 2022): 89–94. http://dx.doi.org/10.54097/hset.v7i.1022.

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Aiming at the problem of cross-infection caused by elevator public buttons during the COVID-19 epidemic, a non-contact elevator button control gesture recognition system based on machine vision is designed. In order to improve the detection speed of gesture recognition, combined with the Spatial Pyramid Pooling (SPP) and replaced the Backbone in YOLOv5 with the lightweight model ShuffleNetV2, an improved YOLOv5_shff algorithm was proposed. After testing, in the task of recognizing gestures, the detection speed of the YOLOv5_shff algorithm is 14% higher than the original model, and the detectio

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Komang Somawirata, I., and Fitri Utaminingrum. "Smart wheelchair controlled by head gesture based on vision." Journal of Physics: Conference Series 2497, no. 1 (2023): 012011. http://dx.doi.org/10.1088/1742-6596/2497/1/012011.

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Abstract Head Gesture Recognition has been developed using a variety of devices that mostly contain a sensor, such as a gyroscope or an accelerometer, for determining the direction and magnitude of movement. This paper explains how to control a smart wheelchair using Head-Gesture Recognition based on Computer Vision. Using the Haar Cascade Algorithm Method for determining the position of the face and nose, determining the order of the head gesture would be easy to do. We classify head gestures to become four, namely: Look down, Look up/center, Turn right and Turn left. The four gesture informa

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Yong Xu. "Research on Dynamic Gesture Recognition and Control System based on Machine Vision." Journal of Electrical Systems 20, no. 2 (2024): 616–28. http://dx.doi.org/10.52783/jes.1215.

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Hand gesture recognition and control is a new type of human-computer interaction that can provide a more convenient and efficient operation mode by utilizing non-contact gesture recognition technology. This paper presents a lightweight dynamic gesture recognition method for intelligent office presentation control. First, we introduce the concept of hand gesture recognition and go over key gesture recognition technologies like classification. The structure, process, and evaluation index of the gesture recognition algorithm are described in detail using a convolutional neural network model. Duri

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Narayanpethkar, Sangamesh. "Computer Vision based Media Control using Hand Gestures." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6642–46. http://dx.doi.org/10.22214/ijraset.2023.52881.

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Abstract: Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. At this time and age, working with a computer in some capacity is a common task. In most situations, the keyboard and mouse are the primary input devices. However, there are several problems associated with excessive usage of the same interaction medium, such as health problems brought on by continuous use of input devices, etc. Humans basically communicate usin

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Bhumkar, Prathamesh. "HAND GESTURE CONTROLLER." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35055.

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In this paper we present an interaction between humans and computer, gesture recognition does play a critical role. While technology has developed to such a level it made possible to communicate with computers with the Gesture Recognition system. Having reached all the best possible ways for data acquisition like cameras, hand Movement now these are of less concern. The desire for human-machine interaction is rapidly growing due to advancements in computer vision technology. Gesture recognition is used extensively in many different types of fields. It indicates that research into vision-based

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Nyirarugira, Clementine, Hyo-rim Choi, and TaeYong Kim. "Hand Gesture Recognition Using Particle Swarm Movement." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/1919824.

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We present a gesture recognition method derived from particle swarm movement for free-air hand gesture recognition. Online gesture recognition remains a difficult problem due to uncertainty in vision-based gesture boundary detection methods. We suggest an automated process of segmenting meaningful gesture trajectories based on particle swarm movement. A subgesture detection and reasoning method is incorporated in the proposed recognizer to avoid premature gesture spotting. Evaluation of the proposed method shows promising recognition results: 97.6% on preisolated gestures, 94.9% on stream gest

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Wang, Xianghan, Jie Jiang, Yingmei Wei, Lai Kang, and Yingying Gao. "Research on Gesture Recognition Method Based on Computer Vision." MATEC Web of Conferences 232 (2018): 03042. http://dx.doi.org/10.1051/matecconf/201823203042.

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Gesture recognition is an important way of human-computer interaction. With time going on, people are no longer satisfied with gesture recognition based on wearable devices, but hope to perform gesture recognition in a more natural way. Computer vision-based gesture recognition can transfer human feelings and instructions to computers conveniently and efficiently, and improve the efficiency of human-computer interaction significantly. The gesture recognition based on computer vision is mainly based on hidden Markov, dynamic time rounding algorithm and neural network algorithm. The process is r

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N. Balaji, G., S. V. Suryanarayana, and C. Veeramani. "Invariant Hand Gesture Recognition System." International Journal of Engineering & Technology 7, no. 4.6 (2018): 299. http://dx.doi.org/10.14419/ijet.v7i4.6.20717.

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Hand gesture recognition plays a vital role in numerous applications, which can run from mobile phones to 3D analysis of anatomy and from gaming to medicinal science. In a large portion of research applications and current business hand gestures recognition, has been implemented by utilizing either vision based or sensor-based gloves strategies where hues, paperclips of synthetic substances are used on to capture the gestures. Another essential issue associated with vision-based procedures is illuminated conditions. The threshold used for the segmentation is changed based on the light variatio

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N. Balaji, G., S. V. Suryanarayana, and C. Veeramani. "Invariant Hand Gesture Recognition System." International Journal of Engineering & Technology 7, no. 4.6 (2018): 299. http://dx.doi.org/10.14419/ijet.v7i4.6.21196.

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Hand gesture recognition plays a vital role in numerous applications, which can run from mobile phones to 3D analysis of anatomy and from gaming to medicinal science. In a large portion of research applications and current business hand gestures recognition, has been implemented by utilizing either vision based or sensor-based gloves strategies where hues, paperclips of synthetic substances are used on to capture the gestures. Another essential issue associated with vision-based procedures is illuminated conditions. The threshold used for the segmentation is changed based on the light variatio

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Jiang, Du, Zujia Zheng, Gongfa Li, et al. "Gesture recognition based on binocular vision." Cluster Computing 22, S6 (2018): 13261–71. http://dx.doi.org/10.1007/s10586-018-1844-5.

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Venkateswarlu, Dr S. China. "Convolutional Neural Network for Hand Gesture Recognition Using 8 Different Gestures." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48820.

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Abstract -- Hand gesture is the main method of communication for people who are hearing-impaired, which poses a difficulty for millions of individuals worldwide when engaging with those who do not have hearing impairments. The significance of technology in enhancing accessibility and thereby increasing the quality of life for individuals with hearing impairments is universally recognized. Therefore, this study conducts a systematic review of existing literature review on hand gesture recognition, with a particular focus on existing methods that address the application of vision, sensor, and hy

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Zheng, Zepei. "Human Gesture Recognition in Computer Vision Research." SHS Web of Conferences 144 (2022): 03011. http://dx.doi.org/10.1051/shsconf/202214403011.

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Human gesture recognition is a popular issue in the studies of computer vision, since it provides technological expertise required to advance the interaction between people and computers, virtual environments, smart surveillance, motion tracking, as well as other domains. Extraction of the human skeleton is a rather typical gesture recognition approach using existing technologies based on two-dimensional human gesture detection. Likewise, I t cannot be overlooked that objects in the surrounding environment give some information about human gestures. To semantically recognize the posture of the

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Heickal, Hasnain, Tao Zhang, and Md Hasanuzzaman. "Computer Vision-Based Real-Time 3D Gesture Recognition Using Depth Image." International Journal of Image and Graphics 15, no. 01 (2015): 1550004. http://dx.doi.org/10.1142/s0219467815500047.

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Gesture is one of the fundamental ways of human machine natural interaction. To understand gesture, the system should be able to interpret 3D movements of human. This paper presents a computer vision-based real-time 3D gesture recognition system using depth image which tracks 3D joint position of head, neck, shoulder, arms, hands and legs. This tracking is done by Kinect motion sensor with OpenNI API and 3D motion gesture is recognized using the movement trajectory of those joints. User to Kinect sensor distance is adapted using proposed center of gravity (COG) correction method and 3D joint p

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Oudah, Munir, Ali Al-Naji, and Javaan Chahl. "Hand Gesture Recognition Based on Computer Vision: A Review of Techniques." Journal of Imaging 6, no. 8 (2020): 73. http://dx.doi.org/10.3390/jimaging6080073.

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Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture

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Newby, Gregory B. "Gesture Recognition Based upon Statistical Similarity." Presence: Teleoperators and Virtual Environments 3, no. 3 (1994): 236–43. http://dx.doi.org/10.1162/pres.1994.3.3.236.

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One of the improvements virtual reality offers traditional human-computer interfaces is that it enables the user to interact with virtual objects using gestures. The use of natural hand gestures for computer input provides opportunities for direct manipulation in computing environments, but not without some challenges. The mapping of a human gesture onto a particular system function is not nearly so easy as mapping with a keyboard or mouse. Reasons for this difficulty include individual variations in the exact gesture movement, the problem of knowing when a gesture starts and ends, and variati

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Faki, Aariz. "Gesture Control Drone: Using Gloves." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41378.

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Gesture-controlled drones represent a significant advancement in human-computer interaction, allowing users to operate drones using simple hand movements without the need for traditional controllers. This technology utilizes computer vision, machine learning, and sensor-based systems to interpret gestures and translate them into drone commands such as takeoff, landing, movement, and hovering. A typical gesture-controlled drone employs a combination of cameras, accelerometers, gyroscopes, and deep learning models to recognize predefined gestures in real-time. Image processing techniques, such a

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Lin, Weikun. "A Systematic Review of Computer Vision-Based Virtual Conference Assistants and Gesture Recognition." Journal of Computer Technology and Applied Mathematics 1, no. 4 (2024): 28–35. https://doi.org/10.5281/zenodo.13889718.

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In the process of introducing gesture recognition, it is essential to explore its technical background and implementation methods. Gesture recognition algorithms based on deep learning perform exceptionally well when processing real-time video streams. These algorithms can extract gesture features and classify them to identify user intentions. For instance, analyzing gesture images using Convolutional Neural Networks (CNN) can effectively enhance recognition accuracy and real-time performance. Additionally, combining optical flow methods with object detection techniques allows for real-time tr

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Wang, Huihui, Bo Ru, Xin Miao, et al. "MEMS Devices-Based Hand Gesture Recognition via Wearable Computing." Micromachines 14, no. 5 (2023): 947. http://dx.doi.org/10.3390/mi14050947.

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Gesture recognition has found widespread applications in various fields, such as virtual reality, medical diagnosis, and robot interaction. The existing mainstream gesture-recognition methods are primarily divided into two categories: inertial-sensor-based and camera-vision-based methods. However, optical detection still has limitations such as reflection and occlusion. In this paper, we investigate static and dynamic gesture-recognition methods based on miniature inertial sensors. Hand-gesture data are obtained through a data glove and preprocessed using Butterworth low-pass filtering and nor

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Yaseen, Oh-Jin Kwon, Jaeho Kim, Jinhee Lee, and Faiz Ullah. "Vision-Based Gesture-Driven Drone Control in a Metaverse-Inspired 3D Simulation Environment." Drones 9, no. 2 (2025): 92. https://doi.org/10.3390/drones9020092.

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Unlike traditional remote control systems for controlling unmanned aerial vehicles (UAVs) and drones, active research is being carried out in the domain of vision-based hand gesture recognition systems for drone control. However, contrary to static and sensor based hand gesture recognition, recognizing dynamic hand gestures is challenging due to the complex nature of multi-dimensional hand gesture data, present in 2D images. In a real-time application scenario, performance and safety is crucial. Therefore we propose a hybrid lightweight dynamic hand gesture recognition system and a 3D simulato

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Yoo, Minjeong, Yuseung Na, Hamin Song, et al. "Motion Estimation and Hand Gesture Recognition-Based Human–UAV Interaction Approach in Real Time." Sensors 22, no. 7 (2022): 2513. http://dx.doi.org/10.3390/s22072513.

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As an alternative to traditional remote controller, research on vision-based hand gesture recognition is being actively conducted in the field of interaction between human and unmanned aerial vehicle (UAV). However, vision-based gesture system has a challenging problem in recognizing the motion of dynamic gesture because it is difficult to estimate the pose of multi-dimensional hand gestures in 2D images. This leads to complex algorithms, including tracking in addition to detection, to recognize dynamic gestures, but they are not suitable for human–UAV interaction (HUI) systems that require sa

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Liu, Zhe, Cao Pan, and Hongyuan Wang. "Continuous Gesture Sequences Recognition Based on Few-Shot Learning." International Journal of Aerospace Engineering 2022 (October 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/7868142.

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A large number of demands for space on-orbit services to ensure the on-orbit system completes its specified tasks are foreseeable, and the efficiency and the security are the most significant factors when we carry out an on-orbit mission. And it can improve human-computer interaction efficiency in operations with proper gesture recognition solutions. In actual situations, the operations are complex and changeable, so the gestures used in interaction are also difficult to predict in advance due to the compounding of multiple consecutive gestures. To recognize such gestures based on computer vis

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Hu, Hong, Jian Gang Chao, and Zai Qian Zhao. "Study of Vision-Based Hand Gesture Recognition System for Astronaut Virtual Training." Advanced Materials Research 998-999 (July 2014): 1062–65. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.1062.

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With the fast development of vision-based hand gesture recognition, it is possible to apply the technology to astronaut virtual training. In order to solve problems of hand gesture recognition in future virtual training and to provide an unrestricted natural training for astronauts, this paper proposed a vision-based hand gesture recognition method, and implemented a hierarchical gesture recognition system to provide a gesture-driven interactive interface for astronaut virtual training system. The experiment results showed that this recognition system can be used to help astronaut training.

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H T, Panduranga, and Mani C. "Non – Vision Based Sensors for Dynamic Hand Gesture Recognition Systems: A Comparative Study." International Journal of Engineering & Technology 7, no. 3.12 (2018): 1175. http://dx.doi.org/10.14419/ijet.v7i3.12.17782.

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Gestures are considered as a type of configuration associated with motion in concerned body part, signifying meaningful information or expressing motion or intending to command and control. Wide ranges of sensors working with different technology are available in market. Gesture recognition process involves steps like data acquisition from sensor, segmentation, an algorithm for taking gesture data as input, an algorithm to extract parameters and algorithm to classify hand gestures. Three - dimensional hand gestures have been widely accepted for advanced applications like creation of virtual wo

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Yu, Cun-jiang, Guo-bao Zhou, Cheng-wei Yan, xiao-ying Ding, and Cheng-shuo Li. "An Improved Gesture Recognition Model Based on Mini-Xception." Journal of Physics: Conference Series 2400, no. 1 (2022): 012020. http://dx.doi.org/10.1088/1742-6596/2400/1/012020.

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Abstract With the rapid development of artificial intelligence technology, gestures have become the mainstream in the field of human-computer interaction because of their simplicity, easy understanding and non-contact. Compared with the early data gloves, the vision-based non-contact gesture recognition interaction method has obvious advantages. However, the variability of the gesture itself, the complexity of the background and the influence of different lighting conditions have impacted the accuracy of gesture recognition. With the rapid development of deep learning technology, gesture recog

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Kotavenuka, Swetha, Harshitha Kodakandla, Nimmakayala Sai Krishna, and Dr S. P. V. Subba Rao. "Hand Gesture Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 331–35. http://dx.doi.org/10.22214/ijraset.2023.48557.

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Abstract: This work presents a computer-vision-based application for recognizing hand gestures. A live video feed is captured by a camera, and a still image is extracted from that feed with the aid of an interface. At least once per count hand gesture (one, two, three, four, and five), the system is trained. After that, the system is given a test gesture to see if it can identify it. Several algorithms that are capable of distinguishing a hand gesture were studied. It was determined that the highest rate of accuracy was achieved by using the computational neural network known as the Alexnet al

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Wu, Bi-Xiao, Chen-Guang Yang, and Jun-Pei Zhong. "Research on Transfer Learning of Vision-based Gesture Recognition." International Journal of Automation and Computing 18, no. 3 (2021): 422–31. http://dx.doi.org/10.1007/s11633-020-1273-9.

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AbstractGesture recognition has been widely used for human-robot interaction. At present, a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize gestures in new domains. For each new domain, it is required to collect and annotate a large amount of data, and the training of the algorithm does not benefit from prior knowledge, leading to redundant calculation workload and excessive time investment. To address this problem, the paper proposes a method that could transfer gesture data in different domains. We use a r

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Kolhe, Ashwini, R. R. Itkarkar, and Anilkumar V. Nandani. "Robust Part-Based Hand Gesture Recognition Using Finger-Earth Mover’s Distance." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (2017): 131. http://dx.doi.org/10.23956/ijarcsse/v7i7/0196.

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Hand gesture recognition is of great importance for human-computer interaction (HCI), because of its extensive applications in virtual reality, sign language recognition, and computer games. Despite lots of previous work, traditional vision-based hand gesture recognition methods are still far from satisfactory for real-life applications. Because of the nature of optical sensing, the quality of the captured images is sensitive to lighting conditions and cluttered backgrounds, thus optical sensor based methods are usually unable to detect and track the hands robustly, which largely affects the p

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Jhaung, Yu-Chiao, Yu-Ming Lin, Chiao Zha, Jenq-Shiou Leu, and Mario Köppen. "Implementing a Hand Gesture Recognition System Based on Range-Doppler Map." Sensors 22, no. 11 (2022): 4260. http://dx.doi.org/10.3390/s22114260.

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There have been several studies of hand gesture recognition for human–machine interfaces. In the early work, most solutions were vision-based and usually had privacy problems that make them unusable in some scenarios. To address the privacy issues, more and more research on non-vision-based hand gesture recognition techniques has been proposed. This paper proposes a dynamic hand gesture system based on 60 GHz FMCW radar that can be used for contactless device control. In this paper, we receive the radar signals of hand gestures and transform them into human-understandable domains such as range

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Tasfia, Rifa, Zeratul Izzah Mohd Yusoh, Adria Binte Habib, and Tousif Mohaimen. "An overview of hand gesture recognition based on computer vision." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 4 (2024): 4636. http://dx.doi.org/10.11591/ijece.v14i4.pp4636-4645.

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Hand gesture recognition emerges as one of the foremost sectors which has gone through several developments within pattern recognition. Numerous studies and research endeavors have explored methodologies grounded in computer vision within this domain. Despite extensive research endeavors, there is still a need for a more thorough evaluation of the efficiency of various methods in different environments along with the challenges encountered during the application of these methods. The focal point of this paper is the comparison of different research in the domain of vision-based hand gesture re

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Zhuang, Guang Li, Jia Lin Tang, Shu Fen Chen, Xi Ying Li, and Bin Hua Su. "Study on the Process of 3D Gesture Recognition Technology Based on Computer Vision." Applied Mechanics and Materials 643 (September 2014): 201–7. http://dx.doi.org/10.4028/www.scientific.net/amm.643.201.

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This paper presents a 3D gesture recognition technology based on machine vision as the center. Based on a large number of experiments, this paper sums up and introduces the existing gesture recognition technology, the key research contents of gesture recognition, as well as the history of development of gesture recognition technology. Then, the paper does research in the main technology of gesture recognition .The experimental results show that the method can realize 3D gesture recognition in video sequences with real-time and stability, even more; it can get better recognition result.

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M, Chandraman, Santhiyakumari N, Ganesh Venkateshwaran S, Damodharan M, Santhiya C, and Subalakshimi V. "EchoGesture Communication: Gesture-based Systems for Individuals with Disabilities." September 2024 6, no. 3 (2024): 253–61. http://dx.doi.org/10.36548/jei.2024.3.004.

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EchoGesture Communication revolutionizes the interaction of differently-abled individuals using hand gestures. People with disabilities often face difficulties in using the conventional electronic gadgets. The proposed study, utilizes sensors, microcontroller, computer vision, and machine learning, to enable real-time recognition of hand gestures, facilitating effective communication. Additionally, Convolutional Neural Network (CNN) is used in the research to achieve accurate gesture recognition. The proposed system allows individuals with disability to communicate effectively using hand gestu

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Wang, Zhaocheng, Guangxuan Hu, Shuo Zhao, Ruonan Wang, Hailong Kang, and Feng Luo. "Local Pyramid Vision Transformer: Millimeter-Wave Radar Gesture Recognition Based on Transformer with Integrated Local and Global Awareness." Remote Sensing 16, no. 23 (2024): 4602. https://doi.org/10.3390/rs16234602.

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A millimeter-wave radar is widely accepted by the public due to its low susceptibility to interference, such as changes in light, and the protection of personal privacy. With the development of the deep learning theory, the deep learning method has been dominant in the millimeter-wave radar field, which usually uses convolutional neural networks for feature extraction. In recent years, transformer networks have also been highly valued by researchers due to their parallel processing capabilities and long-distance dependency modeling capabilities. However, traditional convolutional neural networ

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Chen, Shang-Liang, and Li-Wu Huang. "Using Deep Learning Technology to Realize the Automatic Control Program of Robot Arm Based on Hand Gesture Recognition." International Journal of Engineering and Technology Innovation 11, no. 4 (2021): 241–50. http://dx.doi.org/10.46604/ijeti.2021.7342.

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In this study, the robot arm control, computer vision, and deep learning technologies are combined to realize an automatic control program. There are three functional modules in this program, i.e., the hand gesture recognition module, the robot arm control module, and the communication module. The hand gesture recognition module records the user’s hand gesture images to recognize the gestures’ features using the YOLOv4 algorithm. The recognition results are transmitted to the robot arm control module by the communication module. Finally, the received hand gesture commands are analyzed and exec

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Jiang, Hairong, Juan P. Wachs, and Bradley S. Duerstock. "Integrated vision-based system for efficient, semi-automated control of a robotic manipulator." International Journal of Intelligent Computing and Cybernetics 7, no. 3 (2014): 253–66. http://dx.doi.org/10.1108/ijicc-09-2013-0042.

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Purpose – The purpose of this paper is to develop an integrated, computer vision-based system to operate a commercial wheelchair-mounted robotic manipulator (WMRM). In addition, a gesture recognition interface system was developed specially for individuals with upper-level spinal cord injuries including object tracking and face recognition to function as an efficient, hands-free WMRM controller. Design/methodology/approach – Two Kinect® cameras were used synergistically to perform a variety of simple object retrieval tasks. One camera was used to interpret the hand gestures and locate the oper

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Shah, Pranit, Krishna Pandya, Harsh Shah, and Jay Gandhi. "Survey on Vision based Hand Gesture Recognition." International Journal of Computer Sciences and Engineering 7, no. 5 (2019): 281–88. http://dx.doi.org/10.26438/ijcse/v7i5.281288.

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Raheem, S. Abdul, A. Shiva Sai, B. Keerthana, and Asst Prof Mrs A. Amulya. "Vision and Voice Based Hand Gesture Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 690–94. http://dx.doi.org/10.22214/ijraset.2023.53731.

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Abstract: The development of sign language over time has been astounding. It's unfortunate that this language has some unpleasant side effects. Not everyone can understand spoken sign language when using sign language among a mute or deaf person. For deaf-dumb people, hand gestures and sign language are important forms of communication. Communication is difficult without an interpreter, hence it is necessary to translate sign language so that it is understandable to the general public. The goal is to increase the participation of the deaf and the mute in communication. In order to incorporate

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Satybaldina, Dina, and Gulzia Kalymova. "Deep learning based static hand gesture recognition." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 398. http://dx.doi.org/10.11591/ijeecs.v21.i1.pp398-405.

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Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human–computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For pre-processing and

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Satybaldina, Dina, and Gulzia Kalymova. "Deep learning based static hand gesture recognition." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 398–405. https://doi.org/10.11591/ijeecs.v21.i1.pp398-405.

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Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human-computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For preproce

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Shukla, Akhilesh, Dr Devesh Katiyar, and Mr Gaurav Goel. "Gesture Recognition-based AI Virtual Mouse." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 1583–88. http://dx.doi.org/10.22214/ijraset.2022.40937.

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Abstract: An amazing invention in Computer Technology is the mouse. Nowadays, A Bluetooth mouse or wireless mouse still has some limitations as it requires a battery for power and a dongle to connect to a PC. This issue may be solved in the proposed gesture-based AI virtual mouse by capturing hand motions and revealing hand tips with a webcam or integrated camera, as gestures are a powerful means of communication between people. Based on hand gestures, the computer can be almost controlled and can perform right- clicking, left-clicking, scrolling and computer cursor functions without using the

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Tasfia, Rifa, Mohd Yusoh Zeratul Izzah, Habib Adria Binte, and Tousif Mohaimen. "An overview of hand gesture recognition based on computer vision." An overview of hand gesture recognition based on computer vision 14, no. 4 (2024): 4636–45. https://doi.org/10.11591/ijece.v14i4.pp4636-4645.

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Hand gesture recognition emerges as one of the foremost sectors which has gone through several developments within pattern recognition. Numerous studies and research endeavors have explored methodologies grounded in computer vision within this domain. Despite extensive research endeavors, there is still a need for a more thorough evaluation of the efficiency of various methods in different environments along with the challenges encountered during the application of these methods. The focal point of this paper is the comparison of different research in the dom

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Naveen, Y., and Ch Navya Sree. "GestureFlow: Advanced Hand Gesture Control System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44540.

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Our project "GestureFlow: Advanced Hand Gesture Control System" leverages real-time computer vision and deep learning techniques to create a robust, touchless control interface using hand gestures. The system utilizes MediaPipe Hands for efficient hand landmark detection and processes dynamic hand movements and finger configurations to identify a wide range of intuitive gestures such as swipes, pinches, and specific finger patterns. These gestures are mapped to actions like mouse control, clicks, volume adjustment, media playback, screenshot capture, window management, and many more. The syste

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Jahnavi, Mudili. "Controlling Computer Using Hand Gestures." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49260.

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Abstract: In the realm of Human-Computer Interaction (HCI), the integration of webcams and various sensors has made gesture recognition increasingly accessible and impactful. Hand gestures provide a natural and intuitive mode of communication, enabling seamless interaction between humans and computers. This paper highlights the potential of hand gestures as an effective medium for non-verbal communication and control, with applications spanning across multiple domains. The proposed system leverages image processing techniques, sensor technologies, and computer vision to enable gesture-based co

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Ramadhani, Arief, Achmad Rizal, and Erwin Susanto. "Development of Hand Gesture Based Electronic Key Using Microsoft Kinect." MATEC Web of Conferences 218 (2018): 02014. http://dx.doi.org/10.1051/matecconf/201821802014.

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Computer vision is one of the fields of research that can be applied in a various subject. One application of computer vision is the hand gesture recognition system. The hand gesture is one of the ways to interact with computers or machines. In this study, hand gesture recognition was used as a password for electronic key systems. The hand gesture recognition in this study utilized the depth sensor in Microsoft Kinect Xbox 360. Depth sensor captured the hand image and segmented using a threshold. By scanning each pixel, we detected the thumb and the number of other fingers that open. The hand

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