Robust Dual-Kernel Tracking Using Both Foreground and Background

The kernel-based mean shift tracker outperforms other trackers due to its innovated target representation and efficient optimization strategy. However, this representation relies overmuch on the foreground and thus, decreases the robustness to the background change and clutter. To this point, this paper presents a dual-kernel tracker based on mean shift using both foreground and background. The proposed target representation consists of foreground model and background model, and the optimizing process integrates foreground kernel iteration and background kernel iteration. Experiments indicate that the proposed tracker obtains better performance in coping with background change and clutter.