Bi-directional passenger flow tracking and statistics analysis in station passageways based on an improved Deep-Sort algorithm

Abstract

The normal operation of a integrated hub station is of great significance for the safe operation of the entire city’s transportation network. Accurately monitoring the passenger flow operation status of the station is the fundamental basis for achieving scientific management and control of passenger flow. In response to the urgent need for accurate and real-time detection of passenger flow in station passageways, a Yolov7-based improved Deep-Sort algorithm is proposed to detect and track bi-directional passenger flow in the passageways of integrated hub stations. Based on the Yolov7 detection algorithm, the SimAM attention mechanism was introduced to improve the accuracy of detecting passenger flow in the passageways. On the basis of the Deep-Sort tracking algorithm, the Kalman Filter (KF) method was optimized to make the tracking box of the target more accurate. Meanwhile, the Fast-ReID method was used to improve the long-term tracking of targets, thereby improving the value of IDF1. This algorithm can help to achieve real-time and accurate detection and tracking of bi-directional passenger flow in station passageways. In the event of an abnormal situation, the station staff can react rapidly to improve the station’s operational safety.