Anomalous video event detection using spatiotemporal context

  • Authors:

  • Fan Jiang;Junsong Yuan;Sotirios A. Tsaftaris;Aggelos K. Katsaggelos

  • Affiliations:

  • Department of Electrical Engineering and Computer Science, Northwestern University, 2145 Sheridan Rd., Evanston, IL 60208, USA;School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Ave., Singapore 639798, Singapore;Department of Electrical Engineering and Computer Science, Northwestern University, 2145 Sheridan Rd., Evanston, IL 60208, USA and Department of Radiology, Northwestern University, 737 N Michigan ...;Department of Electrical Engineering and Computer Science, Northwestern University, 2145 Sheridan Rd., Evanston, IL 60208, USA

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2011

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Abstract

Compared to other anomalous video event detection approaches that analyze object trajectories only, we propose a context-aware method to detect anomalies. By tracking all moving objects in the video, three different levels of spatiotemporal contexts are considered, i.e., point anomaly of a video object, sequential anomaly of an object trajectory, and co-occurrence anomaly of multiple video objects. A hierarchical data mining approach is proposed. At each level, frequency-based analysis is performed to automatically discover regular rules of normal events. Events deviating from these rules are identified as anomalies. The proposed method is computationally efficient and can infer complex rules. Experiments on real traffic video validate that the detected video anomalies are hazardous or illegal according to traffic regulations.