Incorporating fully sparse topic models for abnormality detection in traffic videos

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.