A Bus Crowdedness Sensing System Using Deep-Learning Based Object Detection

DOI Web Site 被引用文献1件 参考文献12件
  • HUANG Wenhao
    Graduate school of Media and Governance, Keio University
  • TSUGE Akira
    Graduate school of Media and Governance, Keio University
  • CHEN Yin
    Graduate school of Media and Governance, Keio University Reitaku University
  • OKOSHI Tadashi
    Faculty of Environment and Information Studies, Keio University
  • NAKAZAWA Jin
    Faculty of Environment and Information Studies, Keio University

抄録

<p>Crowdedness of buses is playing an increasingly important role in the disease control of COVID-19. The lack of a practical approach to sensing the crowdedness of buses is a major problem. This paper proposes a bus crowdedness sensing system which exploits deep learning-based object detection to count the numbers of passengers getting on and off a bus and thus estimate the crowdedness of buses in real time. In our prototype system, we combine YOLOv5s object detection model with Kalman Filter object tracking algorithm to implement a sensing algorithm running on a Jetson nano-based vehicular device mounted on a bus. By using the driving recorder video data taken from real bus, we experimentally evaluate the performance of the proposed sensing system to verify that our proposed system system improves counting accuracy and achieves real-time processing at the Jetson Nano platform.</p>

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