Kernel-bayesian framework for object tracking

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 con-ventional 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 de-veloped to accommodate the changes in both appearance and illumination. Ex-perimental results demonstrate that, compared with Bayesian and kernel based tracking frameworks, the proposed algorithm is more efficient and effective.