Kernel-based Target Tracking with Spatial Histogram and Template Drift Correction

Abstract The traditional mean shift based on kernel-histogram is of invariable kernel bandwidth and unable to represent accurately the color distribution of target and eliminate the accumulative tracking errors. To overcome these limitations, a tracking algorithm with the following improved strategies is proposed. Firstly, a modified second-order spatial histogram including spatial and color information is adopted to describe target model and the candidate, and Bhattacharyya distance is derived to evaluate the similarity between them. Secondly, target model is estimated repeatedly according to the target region parameter resulted from template drift correction which avoids the tracking window drifting problem. Then, an affine transform is established combining corner and edge detection to update the tracking window parameters. Finally, a Kalman filter or linear filter is selected to predict the target motion according to the Kalman residual error. The results of experiments show that the proposed algorithm works well and is robust against similarity distraction, scale and orientation variations and short-term occlusion.