DocumentCode :
2426356
Title :
Integrated Detect-Track Framework for Multi-view Face Detection in Video
Author :
Anoop, K.R. ; Anandathirtha, Paresh ; Ramakrishnan, K.R. ; Kankanhalli, Mohan S.
Author_Institution :
Dept of Electr. Eng., Indian Inst. of Sci., Bangalore
fYear :
2008
fDate :
16-19 Dec. 2008
Firstpage :
336
Lastpage :
343
Abstract :
An Experiential sampling and Meanshift tracker based Multi-view face detection in video is proposed in this paper. In this framework, instead of performing face detection at every position in a frame, we determine certain key positions to run the multi-view face detectors. These key positions are statistical samples drawn from a density function that is estimated based on color cues, past detection results, Meanshift tracker results and a temporal continuity model. These samples are then propogated using a Particle filter framework. We use a Meanshift tracker to track faces that are missed by the multiview face detectors. Our framework results in a significant reduction in computation time and accounts for the detection of complete 180 degree pose of the face. We also come up with a novel likelihood measure for track termination, which becomes important when used for detection purposes.
Keywords :
face recognition; particle filtering (numerical methods); video signal processing; experiential sampling; integrated detect-track framework; meanshift tracker; multiview face detection; particle filter; temporal continuity model; video; Computer graphics; Computer vision; Density functional theory; Detectors; Face detection; Image processing; Image sampling; Particle filters; Robustness; Videoconference; Detect-track; Detection; Experiential; Face; Meanshift; Multiview; Particle; TBD; Tracking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision, Graphics & Image Processing, 2008. ICVGIP '08. Sixth Indian Conference on
Conference_Location :
Bhubaneswar
Print_ISBN :
978-0-7695-3476-3
Electronic_ISBN :
978-0-7695-3476-3
Type :
conf
DOI :
10.1109/ICVGIP.2008.91
Filename :
4756090
Link To Document :
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