Hierarchical particle filter tracking algorithm based on multi-feature fusion

A hierarchical particle filter (HPF) framework based on multi-feature fusion is proposed. The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment. In this approach, the Harris algorithm is introduced to detect the corner points of the object, and the corner matching algorithm based on singular value decomposition is used to compute the first-order weights and make particles centralize in the high likelihood area. Then the local binary pattern (LBP) operator is used to build the observation model of the target based on the color and texture features, by which the second-order weights of particles and the accurate location of the target can be obtained. Moreover, a backstepping controller is proposed to complete the whole tracking system. Simulations and experiments are carried out, and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.