Dynamic hand gesture recognition using the skeleton of the hand

被引:45
|
作者
Ionescu, B
Coquin, D
Lambert, P
Buzuloiu, V
机构
[1] Univ Politehn, LEU, Bucharest, Romania
[2] Univ Savoie, LISTIC, F-74016 Annecy, France
关键词
hand gesture recognition; skeleton; orientation histogram; Baddeley distance;
D O I
10.1155/ASP.2005.2101
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper discusses the use of the computer vision in the interpretation of human gestures. Hand gestures would be an intuitive and ideal way of exchanging information with other people in a virtual space, guiding some robots to perform certain tasks in a hostile environment, or interacting with computers. Hand gestures can be divided into two main categories: static gestures and dynamic gestures. In this paper, a novel dynamic hand gesture recognition technique is proposed. It is based on the 2D skeleton representation of the hand. For each gesture, the hand skeletons of each posture are superposed providing a single image which is the dynamic signature of the gesture. The recognition is performed by comparing this signature with the ones from a gesture alphabet, using Baddeley's distance as a measure of dissimilarities between model parameters.
引用
收藏
页码:2101 / 2109
页数:9
相关论文
共 50 条
  • [41] Dynamic Hand Gesture Pattern Recognition Using Probabilistic Neural Network
    Bal, Debasish
    Arfi, Asif Mohammed
    Dey, Sujoy
    2021 IEEE INTERNATIONAL IOT, ELECTRONICS AND MECHATRONICS CONFERENCE (IEMTRONICS), 2021, : 103 - 106
  • [42] Dynamic hand gesture recognition using motion pattern and shape descriptors
    Xing, Meng
    Hu, Jing
    Feng, Zhiyong
    Su, Yong
    Peng, Weilong
    Zheng, Jinqing
    MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 78 (08) : 10649 - 10672
  • [43] Dynamic Hand Gesture Recognition for Mobile Systems Using Deep LSTM
    Sarkar, Ayanava
    Gepperth, Alexander
    Handmann, Uwe
    Kopinski, Thomas
    INTELLIGENT HUMAN COMPUTER INTERACTION, IHCI 2017, 2017, 10688 : 19 - 30
  • [44] Dynamic Hand Gesture Recognition Using HMM-BPNN Model
    Zhou Lu
    Zhang Li-Shuang
    Sun Le
    Zhang Xue-Bo
    2016 IEEE INTERNATIONAL CONFERENCE ON REAL-TIME COMPUTING AND ROBOTICS (IEEE RCAR), 2016, : 422 - 426
  • [45] Virtual hand -: Hand gesture recognition system
    Vamossy, Zoltan
    Toth, Andras
    Benedek, Balazs
    2007 5TH INTERNATIONAL SYMPOSIUM ON INTELLIGENT SYSTEMS & INFORMATICS, 2007, : 82 - 87
  • [46] SHREC 2021: Skeleton-based hand gesture recognition in the wild
    Caputo, Ariel
    Giachetti, Andrea
    Soso, Simone
    Pintani, Deborah
    D'Eusanio, Andrea
    Pini, Stefano
    Borghi, Guido
    Simoni, Alessandro
    Vezzani, Roberto
    Cucchiara, Rita
    Ranieri, Andrea
    Giannini, Franca
    Lupinetti, Katia
    Monti, Marina
    Maghoumi, Mehran
    LaViola Jr, J. Joseph
    Le, Minh-Quan
    Nguyen, Hai-Dang
    Tran, Minh-Triet
    COMPUTERS & GRAPHICS-UK, 2021, 99 : 201 - 211
  • [47] Decoupled Representation Network for Skeleton-Based Hand Gesture Recognition
    Zhong, Zhaochao
    Li, Yangke
    Yang, Jifang
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2022, PT II, 2022, 13530 : 469 - 480
  • [48] Motion feature estimation using bi-directional GRU for skeleton-based dynamic hand gesture recognition
    Tripathi, Reena
    Verma, Bindu
    SIGNAL IMAGE AND VIDEO PROCESSING, 2024, 18 (SUPPL 1) : 299 - 308
  • [49] Dynamic Hand Gesture Recognition With Leap Motion Controller
    Lu, Wei
    Tong, Zheng
    Chu, Jinghui
    IEEE SIGNAL PROCESSING LETTERS, 2016, 23 (09) : 1188 - 1192
  • [50] Real-Time Dynamic Hand Gesture Recognition
    Lai, Hsiang-Yueh.
    Lai, Han-Jheng.
    2014 INTERNATIONAL SYMPOSIUM ON COMPUTER, CONSUMER AND CONTROL (IS3C 2014), 2014, : 658 - 661