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ABSTRACT
In the pursuit of self-driving vehicles, pedestrian recognition plays an integral role. The following work proposes a pedestrian gesture recognition method based on a k-nearest neighbour algorithm combined with pyramid residual module to reduce computation and improve real-time performance of gesture recognition. The method was formulated using a data from Udacity, and subsequently compared with other recognition methods. The experimental results showed that the accuracy of the new method was as high as 92%, which is an improvement over the conventional histogram of oriented gradient method. The proposed method was further verified by other indicators, demonstrating its robustness and generality.
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No potential conflict of interest was reported by the authors.
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Notes on contributors
Ying Zheng
Ying Zheng received her B.S. degree in Software Engineering from Beijing Union University Beijing, China. She is currently pursuing the master’s degree with the Beijing Key Laboratory of Information Service Engineering, Beijing Union University. The main research directions are intelligent driving and intelligent perception. She has many national scholarships and won the outstanding graduates of Beijing, China.
Hong Bao
Hong Bao, received his Ph.D. degree from the School of Computer and Information Technology, Beijing Jiao Tong University, Beijing, China. He is a professor at the Beijing Union University. His current research interests include intelligent control and intelligent vehicles.
Cheng Xu
Cheng Xu is currently a Ph.D. at the Institute of Network Technology in the Beijing University of Posts and Telecommunications (BUPT), China. His research interests include wireless security and the internet capacity of vehicles.