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Scale-adaptive visual tracking with occlusion detection

Occlusion is a challenging problem in visual object tracking. Most state-of-the-art trackers may learn the appearance of the occluding target when it becomes occluded by other objects in the scene. This paper proposes a novel approach of detecting occlusion by dividing the target into several patches and computing the peak-to-sidelobe ratio of every response map. Furthermore, our method can calculate the scale factor by finding the maximum response position of each patch. Experiments are performed on several benchmark challenging sequences. And the results show that our approach outperforms state-of-the-art tracking methods while operating in real-time.