Citation

Wang X, Xu J, Song Y, Zheng Q, Lv J, Yan W, Cai Q, Dai Z. Safety Sci. 2021; 144: e105419.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.ssci.2021.105419

PMID

unavailable

Abstract

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, we 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, we 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.

Language: en

Keywords

Feature point detection; Trajectory clustering; Vehicle behavior analysis; Vehicle segmentation