Robust video object tracking based on multiple kernels with projected gradients

In kernel-based video object tracking, the use of single kernel often suffers from the occlusion. In order to provide more robust tracking performance, multiple inter-related kernels have thus been utilized for tracking in complicated scenarios. This paper presents an innovative method that uses projected gradient to facilitate multiple kernels in finding the best match during tracking under predefined constraints. The adaptive weights are also applied to the kernels in order to efficiently compensate the adverse effect introduced by occlusion. An effective scheme is also incorporated to deal with the scale changing issue during the object tracking. Simulation results demonstrate that the proposed method can successfully track the video object under severe occlusion.