Author/Authors :
Liu, Peng Department of Sports Media and Information Technology - Shandong Sport University, Jinan, Shandong, China , Li, Xiangxiang Department of Optical Science and Engineering - Fudan University, Shanghai, China , Cui, Haiting Department of Sports Media and Information Technology - Shandong Sport University, Jinan, Shandong, China , Li, Shanshan Department of Sports Media and Information Technology - Shandong Sport University, Jinan, Shandong, China , Yafei Yuan, Shanshan Department of Optical Science and Engineering - Fudan University, Shanghai, China
Abstract :
Hand gesture recognition is an intuitive and effective way for humans to interact with a computer due to its high processing speed and recognition accuracy. This paper proposes a novel approach to identify hand gestures in complex scenes by the Single-Shot Multibox Detector (SSD) deep learning algorithm with 19 layers of a neural network. A benchmark database with gestures is used, and general hand gestures in the complex scene are chosen as the processing objects. A real-time hand gesture recognition system based on the SSD algorithm is constructed and tested. The experimental results show that the algorithm quickly identifies humans’ hands and accurately distinguishes different types of gestures. Furthermore, the maximum accuracy is 99.2%, which is significantly important for human-computer interaction application.
