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Abnormal event detection and localisation in traffic videos based on group sparse topical coding

Ahmadi, P ; Sharif University of Technology | 2016

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  1. Type of Document: Article
  2. DOI: 10.1049/iet-ipr.2015.0399
  3. Publisher: Institution of Engineering and Technology , 2016
  4. Abstract:
  5. 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. © The Institution of Engineering and Technology
  6. Keywords:
  7. Algorithms ; Codes (symbols) ; Data mining ; Image retrieval ; Semantics ; Statistics ; Time and motion study ; Abnormal event detections ; Latent Dirichlet allocation ; Probabilistic latent semantic analysis ; Simple and efficient algorithms ; Sparse topical coding ; Traffic videos ; Unsupervised method ; Visual surveillance ; Space time adaptive processing
  8. Source: IET Image Processing ; Volume 10, Issue 3 , 2016 , Pages 235-246 ; 17519659 (ISSN)
  9. URL: http://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2015.0399