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.

翻译：计算机视觉（CV）技术的应用显著推动了基于交通冲突和近碰撞事件的微观交通安全分析，此类分析通常采用替代安全措施（SSM）进行度量。然而，由于视频处理与交通安全建模分属两个独立研究领域，且鲜有研究系统性地弥合二者鸿沟，有必要为交通研究人员和从业者提供相应指导。基于此目标，本文重点回顾了CV技术在基于SSM的交通安全建模中的应用，并提出了最优路径建议。从早期方法到最新模型，本文对用于车辆检测与追踪的CV算法进行了高层次的总结；随后，介绍了用于车辆轨迹提取的视频预处理与后处理技术；详细综述了面向车辆轨迹数据的SSM及其在交通安全分析中的应用；最后，讨论了交通视频处理与基于SSM的安全分析中的实际问题，并提供了现有或潜在的解决方案。本综述旨在协助交通研究人员和工程师选择适合视频处理的CV技术，并依据各类交通安全研究目标合理运用SSM。