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Third IEEE International Workshop on Visual Surveillance (VS'2000)
Application of the Self-Organizing Map to Trajectory Classification
Dublin, Ireland
July 01-July 01
ISBN: 0-7695-0698-4
Jonathan Owens, University of Sunderland
Andrew Hunter, University of Sunderland
This paper presents an approach to the problem of automatically classifying events detected by video surveillance systems; specifically, of detecting unusual or suspicious movements. Approaches to this problem typically involve building complex 3D-models in real-world co-ordinates to provide trajectory information for the classifier. In this paper, we show that analysis of trajectories may be carried out in a model-free fashion, using self-organizing feature map neural networks to learn the characteristics of normal trajectories, and to detect novel ones. Trajectories are represented in 2D image co-ordinates. First and second order motion information is also generated, with moving-average smoothing. This allows novelty detection to be applied on a point-by-point basis in real time, and permits both instantaneous motion and whole trajectory motion to be subjected to novelty detection.
Citation:
Jonathan Owens, Andrew Hunter, "Application of the Self-Organizing Map to Trajectory Classification," vs, pp.77, Third IEEE International Workshop on Visual Surveillance (VS'2000), 2000
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