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IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532
Special Section on Picture Coding and Image Media Processing
A Bus Crowdedness Sensing System Using Deep-Learning Based Object Detection
Wenhao HUANGAkira TSUGE Reitaku University">Yin CHENTadashi OKOSHIJin NAKAZAWA
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2022 Volume E105.D Issue 10 Pages 1712-1720

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Abstract

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.

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© 2022 The Institute of Electronics, Information and Communication Engineers
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