Detection model and experiments of pedestrian fall behavior in cross passages using Baidu AI
Rongyong Zhao, Dong Chengxiao, Wenjie Zhu, Ping Jia, Yunlong Ma, Cuiling Li, Yan Wang · 2022
In public places, the fall behaviors of pedestrians possibly lead to the disturbances of the crowd, and even cause stampedes. This study deals with the problem of identifying anomalous pedestrian behavior in an effort to stop potential stampedes in public areas. In order to detect the fall behavior of pedestrians in public places, Baidu AI was introduced in this paper to detect key skeleton points of pedestrians in a single frame sourced from surveillance videos. The ratio of human height to width and cotangent value of the direction angle of the pedestrian minimum peripheral rectangle are selected as feature vectors. Fall behavior detection model based on SVM is proposed. Experiments are designed and implemented to validate the proposed fall behavior detection model in this paper. This study can provide technical support for early warning and prevention of possible stampede accidents in public places.