Robust 3D head tracking by online feature registration
Author(s) -
Jun-Su Jang,
Takeo Kanade
Publication year - 2008
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1109/afgr.2008.4813307
Subject(s) - artificial intelligence , computer vision , robustness (evolution) , computer science , tracking (education) , frame (networking) , feature tracking , feature (linguistics) , detector , feature extraction , pattern recognition (psychology) , psychology , pedagogy , biochemistry , chemistry , linguistics , philosophy , gene , telecommunications
This paper presents a robust method for tracking the po- sition and orientation of a head in videos. The proposed method can overcome occlusions and divergence problems. We introduce an online registration technique to detect and register feature point of the head while tracking. A set of point features is registered and updated for each reference pose serving a multi-view head detector. The online fea- ture registration rectifies error accumulation and provides fast recovery after occlusion has ended, while preventing di- vergence problem which frequently occurs in conventional frame-to-frame tracking methods. The robustness of the proposed tracker is experimentally shown with video se- quences that include occlusions and large pose variations.
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