traffic scene analysis using hierarchical sparse topical coding

Analyzing motion patterns in traffic videos can be exploited directly to generate highlevel descriptions of the video contents. such descriptions may further be employed in different traffic applications such as traffic phase detection and abnormal event detection. one of the most recent and successful unsupervised methods for complex traffic scene analysis is based on topic models. in this paper, a two-level sparse topical coding (stc) topic model is proposed to analyze traffic surveillance video sequences which contain hierarchical patterns with complicated motions and co-occurrences. the first level stc model is applied to automatically cluster optical flow features into motion patterns. then, the second level stc model is used to cluster motion patterns into traffic phases. experiments on a real world traffic dataset demonstrate the effectiveness of the proposed method against conventional onelevel topic model based methods. the results show that our two-level stc can successfully discover not only the lower level activities but also the higher level traffic phases, which makes a more appropriate interpretation of traffic scenes. furthermore, based on the two-level structure, either activity anomalies or traffic phase anomalies can be detected, which cannot be achieved by the one-level structure.