A spatial-color mean-shift object tracking algorithm with scale and orientation estimation

In this paper, an enhanced mean-shift tracking algorithm using joint spatial-color feature and a novel similarity measure function is proposed. The target image is modeled with the kernel density estimation and new similarity measure functions are developed using the expectation of the estimated kernel density. With these new similarity measure functions, two similarity-based mean-shift tracking algorithms are derived. To enhance the robustness, the weighted-background information is added into the proposed tracking algorithm. Further, to cope with the object deformation problem, the principal components of the variance matrix are computed to update the orientation of the tracking object, and corresponding eigenvalues are used to monitor the scale of the object. The experimental results show that the new similarity-based tracking algorithms can be implemented in real-time and are able to track the moving object with an automatic update of the orientation and scale changes.