Particle Filter-Based Object Tracking Using Adaptive Histogram

Object tracking is a difficult and primary task in many video processing applications. Because of the diversity of various video processing tasks, there exists no optimum method that can perform properly for all applications. Histogram-based particle filtering is one of the most successful object tracking methods. However, for dealing with visual tracking in real world conditions (such as changes in illumination and pose) is still a challenging task. In this paper, we have proposed a color-based adaptive histogram particle filtering method that can update the target model. We have used the Bhattacharyya coefficients to measure the likelihood between two color histograms. Our experimental results show that the proposed method is robust against partial occlusion, rotation, scaling, object deformation, and changes in illumination and pose. It is also fast enough to be used in real-time applications.