TAILIEUCHUNG - Learning the Distribution of Object Trajectories for Event Recognition

The bandwidth demands made on any individual par- ticipant in each multicast tree are quite innocuous. For example, an RSS feed generating 4 KB/hour of updates will cause an interior tree node with 16 children to for- ward less than 20 bytes per second of outbound traffic. Due to the extremely low forwarding overhead, we be- lieve that the motivation for freeloading is very small. In the future, we expect richer content feeds, and con- sequently, the potential incentive for freeloading may increase. Incentives-compatible mechanisms to ensure fair sharing of bandwidth [20] can be applied if most users subscribe to several feeds, which is a common model of RSS usage. We intend. | Learning the Distribution of Object Trajectories for Event Recognition Neil Johnson and David Hogg School of Computer Studies The University of Leeds Leeds LS2 9JT United Kingdom email neilj dch @ 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 whichis 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. 1 Introduction Existing vision systems for surveillance and event recognitionrely on known scenes where objects tend to move in predefined ways see eg. 1 . We wish to identify incidents recognise events and predict object trajectories within unknown scenes where object behaviour is not predefined. We use an open pedestrian scene as an example of such a situation since pedestrians are free to walk wherever they wish. In this paper we develop a model of the probability density functions of possible instantaneous movements and trajectories within a scene. The model is automatically generated by tracking objects over long image sequences. The pdf s are represented by the distribution of prototype vectors which are placed by a neural network implementing vector quantisation. The temporal nature of trajectories is modelled using a type of neuron with short-term memory capabilities. We indicate how the model can be used to recognise atypical movements and thus flag possible incidents of interest and how attaching meaning to areas of the distributions representing similar instantaneous movements and

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