Abstract:The object tracking only using single feature is apt to make errors or lose the target if the illumination and size scale-change. This paper presents a novel adaptive tracking algorithm that fuses multiple features based on posterior probabilities of the particle filter. In this paper uses histogram to characterize color and edge in particle filter algorithms framework. The posterior probabilities of all features are different from each other, which further promotes the fault tolerance ability by cooperation and learning from each other. An extensive number of comparative experiments show that the proposed tracking algorithm is more stable and robust than the single feature and other feature fusion tracking algorithms especially in the case of the illumination and background-change.