Advances and Applications of Computer Vision Techniques in Vehicle Trajectory Generation and Surrogate Traffic Safety Indicators

TL;DR

This paper reviews how computer vision techniques are applied to vehicle trajectory generation and surrogate safety measures, aiding traffic safety analysis and bridging the gap between video processing and traffic safety modeling.

Contribution

It provides a comprehensive review of CV algorithms, video processing techniques, and surrogate safety measures, offering guidance for transportation research and practice.

Findings

Summarizes CV algorithms for vehicle detection and tracking.

Details video pre- and post-processing techniques for trajectory extraction.

Discusses practical issues and solutions in traffic video analysis.

Abstract

The application of Computer Vision (CV) techniques massively stimulates microscopic traffic safety analysis from the perspective of traffic conflicts and near misses, which is usually measured using Surrogate Safety Measures (SSM). However, as video processing and traffic safety modeling are two separate research domains and few research have focused on systematically bridging the gap between them, it is necessary to provide transportation researchers and practitioners with corresponding guidance. With this aim in mind, this paper focuses on reviewing the applications of CV techniques in traffic safety modeling using SSM and suggesting the best way forward. The CV algorithm that are used for vehicle detection and tracking from early approaches to the state-of-the-art models are summarized at a high level. Then, the video pre-processing and post-processing techniques for vehicle trajectory extraction are introduced. A detailed review of SSMs for vehicle trajectory data along with their application on traffic safety analysis is presented. Finally, practical issues in traffic video processing and SSM-based safety analysis are discussed, and the available or potential solutions are provided. This review is expected to assist transportation researchers and engineers with the selection of suitable CV techniques for video processing, and the usage of SSMs for various traffic safety research objectives.