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Proceedings Paper

Space-time correlation filters for human action detection
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Paper Abstract

To automate video surveillance systems, algorithms must be developed to automatically detect and classify basic human actions. Many traditional approaches focus on the classification of actions, which usually assumes prior detection, tracking, and segmentation of the human figure from the original video. On the other hand, action detection is a more desirable paradigm, as it is capable of simultaneous localization and classification of the action. This means that no prior segmentation or tracking is required, and multiple action instances may be detected in the same video. Correlation filters have been traditionally applied for object detection in images. In this paper, we report the results of our investigation using correlation filters for human action detection in videos. Correlation filters have previously been explored for action classification, but this is the first time they are evaluated for the more difficult task of action detection. In addition, we investigate several practical implementation issues, including parameter selection, reducing computational time, and exploring the effects of preprocessing and temporal occlusion (i.e., loss of video frames) on performance.

Paper Details

Date Published: 19 March 2013
PDF: 15 pages
Proc. SPIE 8663, Video Surveillance and Transportation Imaging Applications, 866304 (19 March 2013); doi: 10.1117/12.2001662
Show Author Affiliations
Joseph A. Fernandez, Carnegie Mellon Univ. (United States)
B. V. K. Vijaya Kumar, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 8663:
Video Surveillance and Transportation Imaging Applications
Robert Paul Loce; Eli Saber, Editor(s)

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