Learning semantic scene models from observing activity in visual surveillance.

Dimitios Makris; Tim Ellis

doi:10.1109/tsmcb.2005.846652

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Learning semantic scene models from observing activity in visual surveillance.

Dimitios Makris·Tim Ellis

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Abstract

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 and present results that show the efficiency of our approach. Finally, we describe how the models can be used to support the interpretation of moving objects in a visual surveillance environment.

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Algorithms Artificial Intelligence Computer Simulation Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Models, Biological Models, Statistical Movement Pattern Recognition, Automated Photography Security Measures Signal Processing, Computer-Assisted Subtraction Technique Video Recording Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't

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Dimitios Makris, Tim Ellis. 2005. Learning semantic scene models from observing activity in visual surveillance.. https://doi.org/10.1109/tsmcb.2005.846652

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