Advances and applications of computer vision techniques in vehicle trajectory generation and surrogate traffic safety indicators

•Reviewed computer vision (CV) algorithms for vehicle detection and tracking from traffic videos.•Presented the video pre-processing and post-processing techniques used for vehicle trajectory generation.•Summarized surrogate safety measures (SSM) and conflict-based safety studies using vehicle trajectory data.•Issues have been identified in accurate trajectory extraction, valid SSM calculation, and precise conflict identification, alongside proposed solutions.•Suggested future research directions and opportunities for applying CV technology in traffic safety analysis. 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 algorithms 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.