Automatic analysis and understanding of typical activities and identification of abnormal events in crowded traffic scenes is a fundamental task for traffic video surveillance. In this paper, we address the problem of abnormality detection based on an unsupervised learning approach with Fully Sparse Topic Models (FSTM). The method uses a set of visual features and automatically discovers the activity patterns occurring in complicated scenes. We show how the discovered patterns can be used to detect abnormal events. Furthermore, we compare FSTM with other topic models based on various measures. Experimental results and comparisons on two traffic datasets demonstrate that our approach outperforms other methods in finding meaningful activity patterns and discovers the abnormal events accurately.

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Automatic accident detection is one of the most important tasks for an intelligent transportation system (ITS). In this paper, a new framework for automated traffic accident recognition using topic models is proposed. This framework uses a set of visual features and automatically discovers the motion patterns in traffic scenes. Then, using these learned motion patterns, occurrence of an accident could be detected by various abnormality measures. Results on real video sequences collected from Tehran traffic control center c effectiveness and the applicability of the proposed framework.

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