Robust Object Tracking using Local Kernels and Background Information

The mean shift algorithm has been proved to be efficient for tracking 2D blobs through a video sequence. Even so, this algorithm has certain inherent disadvantages. In this paper, we propose a robust tracking algorithm which overcomes the drawbacks of global color histogram based tracking. We incorporate tracking based only on reliable colors by separating the object from its background. A fast yet robust model update is employed to overcome illumination changes. This algorithm is computationally simple enough to be executed real time and was tested on several complex video sequences. The proposed technique could be easily extended to other tracking algorithms too.