An effective automatic tracking algorithm based on Camshift and Kalman filter

An automatic tracking algorithm based on Camshift and Kalman filter is proposed in this paper to deal with the problems in traditional Camshift algorithm, such as artificial orientation and increasing possibility of tracking failure under occlusion. The inter-frame difference and canny edge detection are combined to segment perfect moving object region accurately, and the center point of the region is obtained as the initial position of the object. With regard to tracking under occlusion, Kalman filter is used to predict the position and velocity of the target. Specifically, the initial iterative position of Camshift algorithm is obtained by Kalman filter in every frame, and then Camshift algorithm is utilized to track the target position. Finally, the parameters of adaptive Kalman filter are updated by the optimal position. However, when severe occlusion appears, the optimal position calculated by Camshift algorithm is inaccurate, and the Kalman filter fails to estimate the coming state effectively. In this situation, the Kalman filter is updated by the Kalman predictive value instead of the value calculated by the Camshift algorithm. The experiment results demonstrate that the proposed algorithm can detect and track the target object accurately and has better robustness to occlusion.