Sequential Topic Modeling for Efficient Analysis of Traffic Scenes

A two-level Sparse Topical Coding (STC) topic model is proposed in this paper for analyzing video sequences of traffic surveillance containing hierarchical patterns accompanied by complicated motions and co-occurrences. In order to automatically cluster optical flow features into motion patterns, a first level STC model is used. Next, the second level STC model is applied for clustering motion patterns into traffic phases. The effectiveness of the suggested method is proved by experiments on a traffic dataset in the real world. Our simulations show that the proposed two-level STC is able to extract the motion patterns and traffic phases accurately, leading to realistic describing the traffic videos.