Statistical analysis of dynamic actions.
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.
Zelnik-Manor
Lihi
L
Computer Vision Lab, Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA. lihi@vision.caltech.edu
Irani
Michal
M
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D007700:Kinetics; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D014741:Video Recording / Q000379:methods*; D016138:Walking / Q000502:physiology*