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Particle filters have been used for visual tracking during long periods because they enable effective estimation for non-linear and non-Gaussian distributions. However, particle filter-based tracking approaches suffer from occlusion and deformation of the target objects, which result in the large difference between the current observations and the target model. Thus, we present a Rao-Blackwellized particle filter (RBPF)-based tracking algorithm that effectively estimates the joint distribution for the target state and the target model; in the proposed method, the target object is tracked by using the particle filter while the target model is simultaneously updated on the basis of the on-line approximation of a mixture of Gaussians. To ensure the robustness to occlusion, we represent the target model by 16 orientation histograms that are spatially divided, and individually update each histogram through a video sequence. We demonstrate the robustness of the proposed method under occlusion and deformation of the target objects.