  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.
