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）技术的应用从交通冲突和近碰撞视角极大推动了微观交通安全分析，这类分析通常采用替代安全度量（SSM）进行评估。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两者间的鸿沟，因此有必要为交通领域研究人员和实践者提供相应指导。基于此目标，本文聚焦于综述CV技术在基于SSM的交通安全建模中的应用，并提出最优实施路径。本文从宏观层面系统梳理了从早期方法到最新模型的车辆检测与追踪CV算法，继而阐述了用于车辆轨迹提取的视频预处理与后处理技术。针对车辆轨迹数据的SSM及其在交通安全分析中的应用，本文进行了详细综述。最后，探讨了交通视频处理与基于SSM的安全分析中的实际问题，并提出了现有或潜在的解决方案。本综述旨在协助交通研究人员与工程师为视频处理选择适配的CV技术，并为不同交通安全研究目标选用合适的SSM。