Closed-Loop Adaptation for Robust Tracking

Abstract

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 but yet difficult solution to the drift problem is to obtain accurate boundaries of the target. We approach such a solution by proposing a novel closed-loop model adaptation framework based on the combination of matting and tracking. In our framework, the scribbles for matting are all automatically generated, 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 is further constructed and successfully updated based on such accurate boundaries. Extensive experiments show that our closed-loop adaptation scheme largely avoids model drift and significantly outperforms other discriminative tracking models as well as video matting approaches.