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CVPR
2007
IEEE

Capturing People in Surveillance Video

9 years 9 months ago
Capturing People in Surveillance Video
This paper presents reliable techniques for detecting, tracking, and storing keyframes of people in surveillance video. The first component of our system is a novel face detector algorithm, which is based on first learning local adaptive features for each training image, and then using Adaboost learning to select the most general features for detection. This method provides a powerful mechanism for combining multiple features, allowing faster training time and better detection rates. The second component is a face tracking algorithm that interleaves multiple view-based classifiers along the temporal domain in a video sequence. This interleaving technique, combined with a correlation-based tracker, enables fast and robust face tracking over time. Finally, the third component of our system is a keyframe selection method that combines a person classifier with a face classifier. The basic idea is to generate a person keyframe in case the face is not visible, in order to reduce the number ...
Rogerio Feris, Ying-li Tian, Arun Hampapur
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Rogerio Feris, Ying-li Tian, Arun Hampapur
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