Object tracking using an adaptive Kalman filter combined with mean shift

Abstract

An object tracking algorithm using an adaptive Kalman filter (KF) combined with mean shift (MS) is proposed. First, the system model of KF is constructed, then the center of the object predicted by KF is used as the initial value of the MS algorithm. The searching result of MS is fed back as the measurement of the adaptive KF, and the estimate parameters of KF are adjusted by the Bhattacharyya coefficient adaptively. The proposed method has the robust ability to track a moving object in consecutive frames under certain real-world complex situations, such as a moving object disappearing partially or totally due to occlusion, fast moving objects, and sudden changes in velocity of a moving object. The experimental results demonstrate that the proposed tracking algorithm is robust and practical.