Crop-Transform-Paste: Self-Supervised Learning for Visual Tracking.

While deep-learning based methods for visual tracking have achieved substantial progress, these schemes entail large-scale and high-quality annotated data for sufficient training. To eliminate expensive and exhaustive annotation, we study self-supervised learning for visual tracking. In this work, we develop the Crop-Transform-Paste operation, which is able to synthesize sufficient training data by simulating various kinds of scene variations during tracking, including appearance variations of objects and background changes. Since the object state is known in all synthesized data, existing deep trackers can be trained in routine ways without human annotation. Different from typical self-supervised learning methods performing visual representation learning as an individual step, the proposed self-supervised learning mechanism can be seamlessly integrated into any existing tracking framework to perform training. Extensive experiments show that our method 1) achieves favorable performance than supervised learning in few-shot tracking scenarios; 2) can deal with various tracking challenges such as object deformation, occlusion, or background clutter due to its design; 3) can be combined with supervised learning to further boost the performance, particularly effective in few-shot tracking scenarios.