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Application of the self-organising map to trajectory classification

By Jonathan Owens and Andrew Hunter


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 coordinates\ud 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-organising\ud feature map neural networks to learn the characteristics of normal trajectories, and to detect novel ones. Trajectories are represented using positional and first and second order motion information, 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

Topics: G760 Machine Learning
Year: 2000
DOI identifier: 10.1109/VS.2000.856860
OAI identifier: oai:eprints.lincoln.ac.uk:1906

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