Abstract
We propose an unsupervised visual tracking method in
this paper. Different from existing approaches using extensive annotated data for supervised learning, our CNN
model is trained on large-scale unlabeled videos in an unsupervised manner. Our motivation is that a robust tracker
should be effective in both the forward and backward predictions (i.e., the tracker can forward localize the target object in successive frames and backtrace to its initial position
in the first frame). We build our framework on a Siamese
correlation filter network, which is trained using unlabeled
raw videos. Meanwhile, we propose a multiple-frame validation method and a cost-sensitive loss to facilitate unsupervised learning. Without bells and whistles, the proposed
unsupervised tracker achieves the baseline accuracy of fully
supervised trackers, which require complete and accurate
labels during training. Furthermore, unsupervised framework exhibits a potential in leveraging unlabeled or weakly
labeled data to further improve the tracking accuracy.