Gesture recognition based on modified Yolov5s
Author:
Affiliation:
1. Beijing Key Laboratory of Field‐Bus Technology and Automation Beijing 100144 People's Republic of China
2. College of Electrical and Control Engineering North China University of Technology Beijing 100144 People's Republic of China
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12477
Reference15 articles.
1. Ni Z. Chen J. Sang N. Gao C. Liu L.:Light YOLO for high‐speed gesture recognition. In:201825th IEEE International Conference on Image Processing (ICIP).Athens Greece pp.3099–3103(2018).10.1109/ICIP.2018.8451766
2. Using Deep Learning in Infrared Images to Enable Human Gesture Recognition for Autonomous Vehicles
3. Yuan S. Han M. Zhang L. Lv J. Zhang F.:Research approach of hand gesture recognition based on improved YOLOV3 network and Bayes classifier. In:2020 The 4th International Conference on Video and Image Processing (ICVIP 2020).New York pp.140–146(2020).https://doi.org/10.1145/3447450.3447473
4. Real‐time hand tracking based on YOLOv4 model and Kalman filter;Xuwei D.;J. China Univ. Posts Telecommun.,2021
5. Dima T.F. Ahmed M.E.:Using YOLOv5 Algorithm to Detect and Recognize American Sign Language. In:2021 International Conference on Information Technology (ICIT) pp.603–607(2021).10.1109/ICIT52682.2021.9491672
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