A Bus Crowdedness Sensing System Using Deep-Learning Based Object Detection
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- HUANG Wenhao
- Graduate school of Media and Governance, Keio University
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- TSUGE Akira
- Graduate school of Media and Governance, Keio University
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- CHEN Yin
- Graduate school of Media and Governance, Keio University Reitaku University
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- OKOSHI Tadashi
- Faculty of Environment and Information Studies, Keio University
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- NAKAZAWA Jin
- Faculty of Environment and Information Studies, Keio University
Abstract
<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>
Journal
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E105.D (10), 1712-1720, 2022-10-01
The Institute of Electronics, Information and Communication Engineers
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Keywords
Details 詳細情報について
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- CRID
- 1390575108414604288
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- ISSN
- 17451361
- 09168532
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- Text Lang
- en
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- Data Source
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- JaLC
- Crossref
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- Abstract License Flag
- Disallowed