Robust Occlusion Handling in Object Tracking

Abstract

In object tracking, occlusions significantly undermine the performance of tracking algorithms. Unlike the existing methods that solely depend on the observed target appearance to detect occluders, we propose an algorithm that progressively analyzes the occlusion situation by exploiting the spatiotemporal context information, which is further double checked by the reference target and motion constraints. This strategy enables our proposed algorithm to make a clearer distinction between the target and occluders than existing approaches. To further improve the tracking performance, we rectify the occlusion-interfered erroneous target location by employing a variant-mask template matching operation. As a result, correct target location can always be obtained regardless of the occlusion situation. Using these techniques, the robustness of tracking under occlusions is significantly promoted. Experimental results have confirmed the effectiveness of our proposed algorithm.