Gesture Recognition Based on YOLO Algorithm

被引：0
|
作者：
Wang F.-H. [1 ,2 ,3 ]
Huang C. [1 ]
Zhao B. [1 ]
Zhang Q. [1 ]
机构：
[1] School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing
[2] The Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing
[3] Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2020年 / 40卷 / 08期
关键词：
Gesture recognition; Mean average precision; YOLO; YOLOv3-tiny-T algorithm;
D O I：
10.15918/j.tbit1001-0645.2019.030
中图分类号：
学科分类号：
摘要：
The application of YOLO (you only look once) algorithm in gesture recognition was studied to improve the speed and accuracy of detection under the background near the skin color, light and shade. Based on the end-to-end detection function, the YOLO algorithm could be arranged to improve operation speed greatly by automatically extracting target feature from convolution neural networks. Considering the excellent performance in target detection process, YOLO algorithm was applied to gesture recognition. Comparing with other application results with YOLO series algorithm, this application result of YOLO algorithm shows better performance in gesture recognition. At the same time, based on a YOLOv3-tiny algorithm, the fast version of YOLOv3 algorithm, a YOLOv3-tiny-T algorithm was proposed. The YOLOv3-tiny-T algorithm can achieve a mean average precision of 92.24% on the UST dataset with five gestures, increasing about 5% combined with YOLOv3-tiny. © 2020, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.
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页码：873 / 879
页数：6
相关论文
共 20 条
  • [11] Redmon J, Divvala S, Girshick R, Et al., You only look once: unified, real-time object detection, Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, pp. 779-788, (2016)
  • [12] Liu W, Anguelov D, Erhan D, Et al., Ssd: single shot multibox detector, Proceedings of European Conference on Computer Vision, pp. 21-37, (2016)
  • [13] Zhang Xun, Chen Liang, Hu Cheng, Et al., A real-time recognition method of static gesture based on deep learning, Modern Computer, 34, pp. 8-13, (2017)
  • [14] Redmon J, Farhadi A., YOLO9000: better, faster, stronger, Proceedings of IEEE Conference on Computer Vision & Pattern Recognition, (2017)
  • [15] Redmon J, Farhadi A., YOLOv3: an incremental improvement, arXiv: Computer Vision and Pattern Recognition, 5, 3, pp. 12-22, (2018)
  • [16] Ni Z, Chen J, Sang N, Et al., Light YOLO for high-speed gesture recognition, Proceedings of the 25th IEEE International Conference on Image Processing, pp. 3099-3103, (2018)
  • [17] Abavisani M, Joze H R, Patel V M, Et al., Improving the performance of unimodal dynamic hand-gesture recognition with multimodal training, Proceedings of Computer Vision and Pattern Recognition, pp. 1165-1174, (2019)
  • [18] Nguyen X S, Brun L, Lezoray O, Et al., Skeleton-based hand gesture recognition by learning SPD matrices with neural networks, Proceedings of IEEE International Conference on Automatic Face & Gesture Recognition (FG), (2019)
  • [19] Lin M, Chen Q, Yan S, Et al., Network in network, arXiv: Neural and Evolutionary Computing, 9, 2, pp. 123-140, (2013)
  • [20] Lin T, Dollar P, Girshick R, Et al., Feature pyramid networks for object detection, Proceedings of Computer Vision and Pattern Recognition, pp. 936-944, (2017)
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