Multi-person vision tracking approach based on human body localization features

Abstract

This paper presents a multi-person vision tracking approach based on human body localization features to address the problem of interactive object localization and tracking in a home monitoring scenario. Firstly, the human body localization model is used to obtain the 3D position of the human body, which is then used to construct the human body motion model based on the Kalman filter method. At the same time, the human appearance model is constructed by fusing human color features and features of the histogram of oriented gradient to better characterize the human body. Secondly, the human body observation model is constructed based on the human body motion model and appearance model to measure the similarities between the human body state sequence in the historical frame and the human body observation result in the current frame, and the cost matrix is then obtained. Thirdly, the Hungarian maximum matching algorithm is employed to match each human body in the current and historical frames, and the exception detection mechanism is simultaneously constructed to further reduce the probability of human tracking and matching failure. Finally, a multi-person vision tracking verification platform was constructed, and the achieved average accuracy was 96.6% in the case of human body overlapping, occlusion, disappearance, and appearance; this verifies the feasibility and effectiveness of the proposed method.