Robust multiple target tracking under occlusion using fragmented mean shift and Kalman filter

Object tracking is critical to visual surveillance and activity analysis. The major issue in multiple visual target tracking is occlusion handling. In this paper, we investigate how to improve the robustness of visual tracking method for multiple target tracking with occlusion. Here we propose weighted fragment based mean shift with Kalman filter with the consideration of color features of the target. Discrete wavelet transform is used to detect the target automatically. Inter frame difference of LL-subband is used for detection of the target. Automatic fragments are acquired by calculating the mean and standard deviation of detected target. Here the weighted fragments are derived from the likelihood function of foreground and background of that particular fragment using color histogram. The output of weighted fragmented mean shift is updated with the help of Kalman filter. The Proposed tracking algorithm has been tested on several challenging videos of different situations and compared with mean shift method using Bhattacharyya coefficients and Bhattacharyya distance. Extensive experiments authenticate the robustness and reliability of the proposed method.