In object tracking applications, it is common for trackers to experience drift problems when the object of interest becomes deformed, which compromises the ability of the tracker to track the object. It is therefore desirable to develop a learning tracker classifier that is robust to deformations. The performance of existing trackers that employ deep classification networks degrades when the amount of training data is limited and does not cover all possible scenarios. While these limitations can be mitigated in part by using larger training datasets, these datasets may still not cover all situations and the positive samples are still monotonous. To overcome this problem, we propose a novel deformation samples generator that generates samples that would normally be difficult for the tracker to classify. In the proposed framework, both the classifier and deformation samples generator learn in a joint manner. Our experiments show that the proposed approach outperforms state-of-the-art methods in both quantitative and qualitative evaluations for the visual object tracking task.
展开▼
