文献类型：会议论文

英文题名：Gesture Recognition Algorithm Based on Lightweight 3DCNN Network

作者：Zhang, Guoping[1]; Ma, Nan[2]; Li, Jiahong[3]; Jiang, Beiyan[3]; Wu, Zhixuan[1]

第一作者：Zhang, Guoping

机构：[1] Beijing Union University, College of Robotics Beijing Union University, Beijing Key Laboratory of Information Service Engineering, Beijing, China; [2] Beijing University of Technology, Faculty of Information Technology, Beijing, China; [3] Beijing Union University, College of Robotics, Beijing, China

第一机构：北京联合大学北京市信息服务工程重点实验室

会议论文集：Proceedings - 2021 17th International Conference on Computational Intelligence and Security, CIS 2021

会议日期：November 19, 2021 - November 22, 2021

会议地点：Chengdu, China

语种：英文

外文关键词：Deep learning

摘要：Aiming at the slow recognition speed of the video- based dynamic gesture recognition method and the large number of parameters in the deep 3D CNN network, we propose a fast gesture recognition method, which is an improved lightweight network Lite-HRNet. Firstly, we convert the 2D lightweight network Lite-HRNet to a 3D CNN network to recognize video-based dynamic gestures while generating fewer parameters. Secondly, this work only converts the first half of the Lite-HRNet network to a 3D CNN network, which can significantly improve the recognition speed of the method. We extensively evaluate our method on the EgoGesture gesture dataset. Experiments show that our proposed method dramatically improves the speed of dynamic gesture recognition and demonstrates superior recognition results. ? 2021 IEEE.