Personnel identification and distribution density analysis of subway station based on convolution neural network

Abstract

In this paper, a method based on convolution neural network and multi-camera fusion is proposed to improve the recognition accuracy of crowd and then the personnel distribution of subway station platform is analyzed. In this method, tensorflow is used as the deep learning training framework and the yolov4 neural network algorithm is used to identify the subway station platform area using three videos synchronously. Through affine transformation and time average statistics, the passenger density of each sub-area is calculated and the distribution of personnel density in the whole area is analyzed. The results show that the number of people recognized by multiple cameras is 58% higher than that by single camera. The new recognition method has high recognition rate for the actual scene with large crowd and more obstacles. Finally some areas with high risk of personnel aggregation have been found, which should be the focus of safety monitoring.