Research Area:  Machine Learning
The study of gesture recognition is closely related to the harmonious development of human-computer interaction, which has important research significance. Aiming at the problems of long detection time and low recognition efficiency of traditional gesture detection algorithm, this paper proposes a gesture recognition model based on the combination of improved the YOLOV3 network and the Bayes classifier. The spatial transformer network is used to improve the YOLOV3 network for processing gesture information and extract key gesture features, so as to solve the problem of data vulnerability while maintaining the depth extraction of feature information. Then, the features are input into the combined model of PCA network and Bayes classifier for predicting gesture categories with reducing the dimension of data and improve the classification accuracy. Finally, the comparison test is performed using the public and self-made dataset, and experimental result illustrate that the propose algorithm can improve detection accuracy with higher effectiveness.
Keywords:  
Hand Gesture Recognition
Improved YOLOV3 network
Bayes classifier
Machine learning
Author(s) Name:  Shuai Yuan , Manfei Han , Lili Zhang , Jiaqi Lv , Feng Zhang
Journal name:  
Conferrence name:  ICVIP 2020: 2020 The 4th International Conference on Video and Image Processing
Publisher name:  ACM
DOI:  10.1145/3447450.3447473
Volume Information:  
Paper Link:   https://dl.acm.org/doi/abs/10.1145/3447450.3447473