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Detection of passenger flow on and off buses based on video images and YOLO algorithm

  • 1193: Intelligent Processing of Multimedia Signals
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Abstract

Bus passenger flow information is very important as a reference data for bus company line optimization, schedule scheduling basis, and passenger travel mode arrangement. With the development of image processing technology, it has become a current research trend to count passenger flow with the help of surveillance video of passengers getting on and off the bus. The specific research contents of this paper based on video image detection and statistics of passengers are as follows:(1) Collect head target image samples through a variety of ways, including 3960 positive head target samples and 4150 negative head target samples, which together constitute the head target feature database. (2) Established a head target detection model based on deep learning. First, the labeling of the head target training data set is completed. Then, after 15,000 iterations of model training, the YOLOv3 head target detection network model was obtained, with a recall rate of 92.12% and an accuracy rate of 89.71%. (3) A multi-target matching tracking algorithm based on the combination of Cam-shift and YOLOv3 is proposed. First, the Cam-shift algorithm is used to track the head target. Secondly, the head target tracking data and the YOLOv3 detection data are combined to solve the problem of drift during the tracking of the Cam-shift algorithm through the data association matching method based on the minimum distance, and then combined with the time constraint, a passenger location information judgment rule is proposed. Optimize the error and missed detection in the process of head target detection and tracking, and improve the reliability of passenger trajectory tracking. (4) A statistical algorithm for the detection of passengers getting on and off the bus is proposed. First, the trajectory of passengers in the bus boarding and disembarking area is analyzed, and a process for judging passengers’ boarding and boarding behavior is proposed. At the same time, a passenger position information judgment rule is proposed according to the different situations of whether there are new passengers or missing passengers, so as to optimize the problem of wrong detection and missing detection in the process of head target detection and tracking. (5) Finally, experiments are carried out in actual bus scenes and simulation scenes. The experiment proves that the statistical algorithm for the detection of passengers getting on and off the bus proposed in this paper has good detection, tracking and statistics effects in bus scenes and simulation scenes.

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Acknowledgements

This work is supported by the National Key R&D Program of China (2019YFB1600200), National Natural Science Foundation of China (71871011, 71890972/71890970, 71621001), and the first batch of science and technology projects of Jingde Expressway(JD-202014).

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Authors and Affiliations

  1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, China

    Jiandong Zhao, Lanxin Jiao & Zhimin Zhao

  2. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing, 100044, China

    Jiandong Zhao

  3. Hebei Provincial Communication Planning and Design Institute, Shijiazhuang, 050000, China

    Chunjie Li

  4. School of Mechanical and Electronic Control Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Zhou Xu

  5. Jingde Temporary Construction Office of Hebei Expressway, Baoding, 071051, China

    Zhibin Wang

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  1. Jiandong Zhao
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  2. Chunjie Li
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  4. Lanxin Jiao
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Correspondence to Jiandong Zhao.

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Zhao, J., Li, C., Xu, Z. et al. Detection of passenger flow on and off buses based on video images and YOLO algorithm. Multimed Tools Appl 81, 4669–4692 (2022). https://doi.org/10.1007/s11042-021-10747-w

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  • DOI: https://doi.org/10.1007/s11042-021-10747-w

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