Abstract
This paper addresses the problem of automatically learning common behaviors from long time observations of a scene of interest, with the purpose of classifying actions and, possibly, detecting anomalies. Unsupervised learning is used as an effective way to extract information from the scene with a very limited intervention of the user. The method we propose is rather general, but fits very naturally to a video-surveillance scenario, where the same environment is observed for a long time, usually from a distance. The experimental analysis is based on thousands of dynamic events acquired by three-weeks observations of a single-camera video-surveillance system installed in our department.
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Noceti, N., Odone, F. (2011). Unsupervised Video Surveillance. In: Koch, R., Huang, F. (eds) Computer Vision – ACCV 2010 Workshops. ACCV 2010. Lecture Notes in Computer Science, vol 6468. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22822-3_9
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DOI: https://doi.org/10.1007/978-3-642-22822-3_9
Publisher Name: Springer, Berlin, Heidelberg
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