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This paper considers the problem of automatically learning an activity-based semantic scene model from a stream of video data. A scene model is proposed that labels regions according to an identifiable activity in each region, such as entry/exit zones, junctions, paths, and stop zones. We present several unsupervised methods that learn these scene elements(More)
We report an investigation to determine the topology of an arbitrary network of video cameras observing an environment. The topology is learnt in an unsupervised manner by temporal correlation of objects transiting between adjacent camera viewfields. We extract this information in two steps, firstly identifying the principal entry and exit zones associated(More)
This paper investigates the task of identifying frequently-used pathways from video sequences of natural outdoor scenes. Path models are adaptively learnt from the accumulation of trajectory data over many image frames. Labelled paths are used as an efficient means for compressing the trajectory data for logging purposes. In addition, the path models are(More)