Kernel-Bayesian framework for object tracking

Authors:
Xiaoqin Zhang;Weiming Hu;Guan Luo;Steve Maybank
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Beijing, China;School of Computer Science and Information Systems, Birkbeck College, London, UK
Venue:
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Year:
2007

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Abstract

This paper proposes a general Kernel-Bayesian framework for object tracking. In this framework, the kernel based method--mean shift algorithm is embedded into the Bayesian framework seamlessly to provide a heuristic prior information to the state transition model, aiming at effectively alleviating the heavy computational load and avoiding sample degeneracy suffered by the conventional Bayesian trackers. Moreover, the tracked object is characterized by a spatial-constraint MOG (Mixture of Gaussians) based appearance model, which is shown more discriminative than the traditional MOG based appearance model. Meantime, a novel selective updating technique for the appearance model is developed to accommodate the changes in both appearance and illumination. Experimental results demonstrate that, compared with Bayesian and kernel based tracking frameworks, the proposed algorithm is more efficient and effective.