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Robust Head Tracking Based on Multiple Cues Fusion in the Kernel-Bayesian Framework

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4 Author(s)
Xiaoqin Zhang ; Inst. of Intell. Syst. & Decision, Wenzhou Univ., Wenzhou, China ; Weiming Hu ; Hujun Bao ; Maybank, S.

This paper presents a robust head tracking algorithm based on multiple cues fusion in a kernel-Bayesian framework. In this algorithm, the object to be tracked is characterized using a spatial-constraint mixture of the Gaussians-based appearance model and a multichannel chamfer matching-based shape model. These two models complement each other and their combination is discriminative in distinguishing the object from the background. A selective updating technique for the appearance model is employed to accommodate appearance and illumination changes. Meantime, the kernel method-mean shift algorithm is embedded into the Bayesian framework to give a heuristic prediction in the hypotheses generation process. This alleviates the great computational load suffered by conventional Bayesian trackers. Experimental results demonstrate that the proposed algorithm is effective.

Published in:

Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:23 ,  Issue: 7 )
Biometrics Compendium, IEEE
RFIC Virtual Journal, IEEE
RFID Virtual Journal, IEEE