Learning the distribution of object trajectories for event recognition
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Proceedings of the ACM International Conference on Image and Video Retrieval
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Track based relevance feedback for tracing persons in surveillance videos
Computer Vision and Image Understanding
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Automatic and real-time identification of unusual incidents is important for event detection and alarm systems. In today's camera surveillance solutions video streams are displayed on-screen for human operators, e.g. in large multi-screen control centers. This in turn requires the attention of operators for unusual events and urgent response. This paper presents a method for the automatic identification of unusual visual content in video streams real-time. In contrast to explicitly modeling specific unusual events, the proposed approach incrementally learns the usual appearances from the visual source and simultaneously identifies potential unusual image regions in the scene. Experiments demonstrate the general applicability on a variety of large-scale datasets including different scenes from public web cams and from traffic monitoring. To further demonstrate the real-time capabilities of the unusual scene detection we actively control a Pan-Tilt-Zoom camera to get close up views of the unusual incidents.