Co-training framework of generative and discriminative trackers with partial occlusion handling

Partial occlusion is a challenging problem in object tracking. In online visual tracking, it is the critical factor causing drift. To address this problem, we propose a novel approach using a co-training framework of generative and discriminative trackers. Our approach is able to detect the occluding region and continuously update both the generative and discriminative models using the information from the non-occluded part. The generative model encodes all of the appearance variations using a low dimension subspace, which helps provide a strong reacquisition ability. Meanwhile, the discriminative classifer, an online support vector machine, focuses on separating the object from the background using a Histograms of Oriented Gradients (HOG) feature set. For each search window, an occlusion likelihood map is generated by the two trackers through a co-decision process. If there is disagreement between these two trackers, the movement vote of KLT local features is used as a referee. Precise occlusion segmentation is performed using MeanShift. Finally, each tracker recovers the occluded part and updates its own model using the new non-occluded information. Experimental results on challenging sequences with different types of objects are presented. We also compare with other state-of-the-art methods to demonstrate the superiority and robustness of our tracking framework.