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N. Johnson and D. C. Hogg
ABSTRACT
The advent in recent years of robust, real-time, model-based tracking techniques for rigid and non-rigid moving objects has made automated surveillance and event recognition a possibility. We present a statistically based model of object trajectories which is learnt from image sequences. Trajectory data is supplied by a tracker using Active Shape Models, from which a model of the distribution of typical trajectories is learnt. Experimental results are included to show the generation of the model for trajectories within a pedestrian scene. We indicate how the resulting model can be used for the identification of incidents, event recognition and trajectory prediction. 
ECVision indexed and annotated bibliography of cognitive computer vision publications
This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1
The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
Learning the distribution of object trajectories for event recognitionSite generated on Friday, 06 January 2006