Computer vision-based detection model for passenger flows inside urban rail transit stations

Yao Chen

doi:10.1117/12.2668571

16 February 2023 Computer vision-based detection model for passenger flows inside urban rail transit stations

Yao Chen

Proceedings Volume 12591, Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022); 1259138 (2023) https://doi.org/10.1117/12.2668571
Event: 6th International Conference on Traffic Engineering and Transportation System (ICTETS 2022), 2022, Guangzhou, China

Abstract

The study of short-term passenger flow of rail transit is based on the passenger flow data, usually taking the station as the smallest unit and can only grasp the passenger flow law between stations macroscopically. In order to study the microscopic passenger flow law in different scenes within the station, it is necessary to obtain the passenger flow sequence of the corresponding scene. Therefore, we propose an end-to-end refined short-term passenger flow identification model based on computer vision named PAX-Detect. The structure of the model are as follows: 1). Take pictures of the scene in the station and manually mark the passenger's head; 2). Using annotated datasets to train the YOLOv5 algorithm and pruning optimization;3). YOLOv5 and Deep SORT algorithm are used to detect and track passengers in the video, and the statistical results of the number of passengers are output in real-time. The experiment is carried out on the video data of the escalator entrance of a subway station in Beijing. The experimental results show that the accuracy of the real-time statistical algorithm for passenger numbers reaches 99.4% in the test scenario. The proposed model can identify the microscopic passenger flow in the scene of the rail transit station in real-time and has a certain application prospect.

Citation Download Citation

Yao Chen "Computer vision-based detection model for passenger flows inside urban rail transit stations", Proc. SPIE 12591, Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022), 1259138 (16 February 2023); https://doi.org/10.1117/12.2668571

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