Vehicle behavior analysis using reconstructed 3D parameters for road safety

Road traffic safety is a very important issue in the field of intelligent transportation system (ITS). Vehicle segmentation and behavior analysis are an significant part for solving this problem. However, 2D image detection technology is difficult to reconstruct missing information of damaged image. In this paper, a bottom-up analysis method is employed to study the related technical problems, and it will provide a strong data foundation for road traffic safety. Firstly, the M-BRISK descriptor algorithm is proposed to describe the local feature points. Secondly, the authors propose a 3D feature analysis method based on rigid motion constraints for vehicle trajectory. Thirdly, a similarity measure method is proposed for trajectory clustering. Finally, the authors used the obtained 3D information of vehicles to analyze the vehicle behavior to find the abnormal vehicles for road traffic safety. The experimental results confirm that the M-BRISK descriptor performs well comparing with the state-of-the-art feature descriptors, and the proposed clustering method improves the accuracy of the trajectory clustering. Moreover, the vehicle motion information contained in the trajectory data can be analyzed to recognize vehicle behavior. The presented work in this paper provides an important foundation for vehicle abnormal behavior detection for road traffic safety.

• Record URL:
  https://doi.org/10.1016/j.ssci.2021.105419
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0925753521002630
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09257535
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wang, Xuan
  • Xu, Jindong
  • Song, Yongchao
  • Zheng, Qiang
  • Lv, Jun
  • Yan, Weiqing
  • Cai, Qing
  • Dai, Zhe
• Publication Date: 2021-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • Safety Science
  • Volume: 144
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0925-7535
  • Serial URL: http://www.sciencedirect.com/science/journal/09257535

Subject/Index Terms

• TRT Terms: Cluster analysis; Driver performance; Image analysis; Traffic safety; Vehicle trajectories
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01783314
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 28 2021 11:30AM