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Issue No.09 - September (2006 vol.28)
pp: 1530-1535
Real-world action recognition applications require the development of systems which are fast, can handle a large variety of actions without a priori knowledge of the type of actions, need a minimal number of parameters, and necessitate as short as possible learning stage. In this paper, we suggest such an approach. We regard dynamic activities as long-term temporal objects, which are characterized by spatio-temporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences which captures the similarities in their behavioral content. This measure is nonparametric and can thus handle a wide range of complex dynamic actions. Having a behavior-based distance measure between sequences, we use it for a variety of tasks, including: video indexing, temporal segmentation, and action-based video clustering. These tasks are performed without prior knowledge of the types of actions, their models, or their temporal extents.
Action recognition, video indexing, temporal segmentation.
Lihi Zelnik-Manor, Michal Irani, "Statistical Analysis of Dynamic Actions", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.28, no. 9, pp. 1530-1535, September 2006, doi:10.1109/TPAMI.2006.194
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