Abnormal event detection and localisation in traffic videos based on group sparse topical coding. Issue 3 (1st March 2016) - Ahmadi, Parvin; Tabandeh, Mahmoud; Gholampour, Iman
Abstract : In visual surveillance, detecting and localising abnormal events are of great interest. In this study, an unsupervised method is proposed to automatically discover abnormal events occurring in traffic videos. For learning typical motion patterns occurring in such videos, a group sparse topical coding (GSTC) framework and an improved version of it are applied to optical flow features extracted from video clips. Then a very simple and efficient algorithm is proposed for GSTC. It is shown that discovered motion patterns can be employed directly in detecting abnormal events. A variety of abnormality metrics based on the resulting sparse codes for detection of abnormality are investigated. Experiments show that the result of the approach in detection and localisation of abnormal events is promising. In comparison with other usual methods (probabilistic latent semantic analysis, latent Dirichlet allocation, sparse topical coding (STC) and improved STC), according to the values of area under ROC, the proposed method achieves at least 14% improvement in abnormal event detection.