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

Wenhao Huang, Akira Tsuge, Yin Chen, Tadashi Okoshi, Jin Nakazawa

研究成果: Article査読

抄録

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 learningbased 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.

本文言語English
ページ(範囲)1712-1720
ページ数9
ジャーナルIEICE Transactions on Information and Systems
E105D
10
DOI
出版ステータスPublished - 2022 10月

ASJC Scopus subject areas

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学
  • 人工知能

フィンガープリント

「A Bus Crowdedness Sensing System Using Deep-Learning Based Object Detection」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル