Statistical analysis of dynamic actions.

Lihi Zelnik-Manor; Michal Irani

doi:10.1109/tpami.2006.194

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Statistical analysis of dynamic actions.

Lihi Zelnik-Manor·Michal Irani

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Abstract

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.

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Algorithms Artificial Intelligence Humans Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Kinetics Movement Pattern Recognition, Automated Video Recording Walking Journal Article Research Support, Non-U.S. Gov't

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Lihi Zelnik-Manor, Michal Irani. 2006. Statistical analysis of dynamic actions.. https://doi.org/10.1109/tpami.2006.194

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