Contrastive Cycle Consistency Learning for Unsupervised Visual Tracking

Unsupervised visual tracking has received increasing attention recently. Existing unsupervised visual tracking methods mainly exploit the cycle consistency of sequential images to learn an unsupervised representation for target objects. Due to the small appearance changes between consecutive images, existing unsupervised deep trackers compute the cycle consistency loss over a temporal span to reduce data correlation. However, this causes the learned unsupervised representation not robust to abrupt motion changes as the rich motion dynamics between consecutive frames are not exploited. To address this problem, we propose to contrastively learn cycle consistency over consecutive frames with data augmentation. Specifically, we first use a skipping frame scheme to perform step-by-step cycle tracking for learning unsupervised representation. We then perform unsupervised tracking by computing the contrastive cycle consistency over the augmented consecutive frames, which simulates the challenging scenarios of large appearance changes in visual tracking. This helps us make full use of the valuable temporal motion information for learning robust unsupervised representation. Extensive experiments on large-scale benchmark datasets demonstrate that our proposed tracker significantly advances the state-of-the-art unsupervised visual tracking algorithms by large margins.