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Statistical analysis of dynamic actions

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2 Author(s)
Zelnik-Manor, L. ; Dept. of Eng. & Appl. Sci., California Inst. of Technol., Pasadena, CA ; Irani, M.

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

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:28 ,  Issue: 9 )

Date of Publication:

Sept. 2006

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