Deep Learning-Based Computer Vision for Surveillance in ITS: Evaluation of State-of-the-Art Methods

Intelligent transportation system (ITS) collects numerous data for analysis of the transportation system. The data can be used for providing services for travellers and traffic controllers in the ITS and optimizing it, for the purpose of making the transportation more efficient and safer. Due to the wide and flexible employment of video cameras in visual surveillance system (VSS), mature edge-cloud resource scheduling for data transmission and analysis, and the fast development of deep learning, computer vision (CV) methods have been employed in the visual-based ITS services successfully. In this paper, the authors discuss the edge-cloud surveillance resource scheduling for the CV methods and review the deep learning-based CV methods in the VSS, including detection, classification, and tracking methods, for better understanding of the relationship between the CV-based ITS services and these methods. The authors experimentally compare several state-of-the-art deep learning-based methods, which have been successfully applied in the CV fields under the ITS scenario, on their performance, inference speed, computational quantity, and model size. According to the comparisons, the authors propose four main challenges of the deep learning-based CV methods applied in the services, as a discussion of the future research directions. Code are available at https://github.com/PRIS-CV/DL-CV-ITS.

• Record URL:
  http://dx.doi.org/10.1109/TVT.2021.3065250
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Copyright © 2021, IEEE.
• Authors:
  • Xie, Jiyang
  • Zheng, Yixiao
  • Du, Ruoyi
  • Xiong, Weiyu
  • Cao, Yufei
  • Ma, Zhanyu
  • Cao, Dongpu
  • Guo, Jun
• Publication Date: 2021-4

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 3027-3042
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 70
  • Issue Number: 4
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Cameras; Data analysis; Disasters and emergency operations; Evaluation; Intelligent transportation systems; Machine learning; Machine vision; State of the art; Surveillance; Visualization
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01773299
• Record Type: Publication
• Files: TRIS
• Created Date: May 27 2021 12:37PM