Real-time human tracking based on switching linear dynamic system combined with adaptive Meanshift tracker

Real-time human tracking in complex environments usually presents many challenges, such as partial/complete occlusions caused by irregular motion and similar-color distractors. Switching Linear Dynamic System(SLDS) and Meanshift(MS) are two successful approaches, although both have inherent deficiencies, like accumulated prediction and correction errors in SLDS, uncoded attentional and spatial information in Meanshift, respectively. In this paper, a spatial-representive Meanshift and a joint attentional feature histogram from bottom-up and top-down attention models are used to make the tracker more adaptive. Then a tracking algorithm is proposed as adaptive Meanshift embedded SLDS, where four transition matrixes A(s_t) handle partial/complete occlusions with irregular motion under the framework, and adaptive Meanshift solves short occlusions of similar-color distractors in local search for higher accuracy. Experiments show that this method can work more robustly for partial/complete occlusions among multiple persons compared with adaptive Meanshift and Meanshift embedded SLDS.