IEICE TRANSACTIONS on Information
A Bus Crowdedness Sensing System Using Deep-Learning Based Object Detection
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.10 pp.1712-1720
- Publication Date
- 2022/10/01
- Publicized
- 2022/06/23
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022PCP0007
- Type of Manuscript
- Special Section PAPER (Special Section on Picture Coding and Image Media Processing)
- Category
Authors
Wenhao HUANG
Keio University
Akira TSUGE
Keio University
Yin CHEN
Keio University,Reitaku University
Tadashi OKOSHI
Keio University
Jin NAKAZAWA
Keio University
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Wenhao HUANG, Akira TSUGE, Yin CHEN, Tadashi OKOSHI, Jin NAKAZAWA, "A Bus Crowdedness Sensing System Using Deep-Learning Based Object Detection" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 10, pp. 1712-1720, October 2022, doi: 10.1587/transinf.2022PCP0007.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022PCP0007/_p
Copy
@ARTICLE{e105-d_10_1712,
author={Wenhao HUANG, Akira TSUGE, Yin CHEN, Tadashi OKOSHI, Jin NAKAZAWA, },
journal={IEICE TRANSACTIONS on Information},
title={A Bus Crowdedness Sensing System Using Deep-Learning Based Object Detection},
year={2022},
volume={E105-D},
number={10},
pages={1712-1720},
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.},
keywords={},
doi={10.1587/transinf.2022PCP0007},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - A Bus Crowdedness Sensing System Using Deep-Learning Based Object Detection
T2 - IEICE TRANSACTIONS on Information
SP - 1712
EP - 1720
AU - Wenhao HUANG
AU - Akira TSUGE
AU - Yin CHEN
AU - Tadashi OKOSHI
AU - Jin NAKAZAWA
PY - 2022
DO - 10.1587/transinf.2022PCP0007
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2022
AB - 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.
ER -
English
