The Community for Technology Leaders
Green Image
Issue No. 09 - September (2006 vol. 28)
ISSN: 0162-8828
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. , pp. 1530-1535, September 2006, doi:10.1109/TPAMI.2006.194
83 ms
(Ver 3.3 (11022016))