Robust Visual Tracking With Context-Based Active Occlusion Recognition

Occlusion is a great challenge for target model update in visual tracking. The target template may be corrupted by non-object information in the process of online learning due to occlusion. In this paper, we propose a context-based active occlusion recognition framework that can be integrated with various tracking approaches. The basic idea is to recognize the occlusion patches by actively tracking context patches and distinguish them from target based on a target model that integrates the information of both target and context. The framework consists of a context patch tracker, an occlusion patch recognizer and a local target template updater. The context patch tracker locates the context patches. The occlusion patch recognizer identifies the context patches occluding target. The local target template updater updates only non-occluded regions of the target template. In this way, the target template not only learns current target features but also avoids context corruption caused by occlusion. The experimental results show that our framework can improve the tracking performance in cases of heavy occlusion.