Scribble Tracker: A Matting-Based Approach for Robust Tracking
Model updating is a critical problem in tracking. Inaccurate extraction of the foreground and background information in model adaptation would cause the model to drift and degrade the tracking performance. The most direct yet difficult solution to the drift problem is to obtain accurate boundaries of the target. We approach such a solution by proposing a novel model adaptation framework based on the combination of matting and tracking. In our framework, coarse tracking results automatically provide sufficient and accurate scribbles for matting, which makes matting applicable in a tracking system. Meanwhile, accurate boundaries of the target can be obtained from matting results even when the target has large deformation. An effective model combining short-term features and long-term appearances is further constructed and successfully updated based on such accurate boundaries. The model can successfully handle occlusion by explicit inference. Extensive experiments show that our adaptation scheme largely avoids model drift and significantly outperforms other discriminative tracking models.