Hierarchical search strategy in particle filter framework to track infrared target

A target of interest may exhibit significant appearance variations because of its complex maneuvers, ego-motion of the camera platform, etc. Currently, target tracking in forward-looking infrared (FLIR) sequences is still a challenging problem in the field of computer vision. Although many efforts have been devoted, there are still some issues to be addressed. First, state particles generated by prior information cannot approximate the probability density function well when the target state changes obviously. Second, plenty of particles have to be employed to obtain satisfying estimation of target state which will cause heavy computational burden in turn. In this paper, a hierarchical search strategy (HS tracker) is proposed to track infrared target in the particle filter framework, and there are two observation models employed to locate the target robustly. In the first stage, a saliency map leads the redistributed state particles to cover the salient areas that can provide a rough prediction of the target areas. In the second stage, sparse representation is employed to search for a subset of true ones from all the target candidates; thus, only efficient state particles are used to estimate the target state. The proposed method is tested on numerous FLIR sequences from the US army aviation and missile command database, and experimental results demonstrate the excellent performance.