Robust 3 D Head Tracking by View-based Feature Point Registration

This paper presents a robust method for tracking the position and orientation of a head in videos. Head tracking is regarded as a 3D rigid body tracking and a cylinder model is used to obtain 2Dto-3D correspondences. We introduce a view-based feature point 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. Since tracking-by-detection approach is used in our method, the proposed tracker utilizes view-based model more efficiently than previous view-based model approaches. The view-based feature point registration rectifies error accumulation and provides fast recovery after occlusion has ended, while preventing divergence problem which frequently occurs in conventional frame-to-frame tracking methods. Kalman filter is used to incorporate motion between successive frames and the estimated pose with the view-based head model. Although we focus on the robustness of tracker, the accuracy of the proposed tracker is comparable to previous studies. The robustness of the proposed tracker is experimentally shown with a large number of video sequences that include various situations such as slow motion, fast motion and occlusions.

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