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.

该论文提出了一种无监督视觉跟踪方法，使用 CNN 模型和监督学习相反，采用大规模未标记视频进行无监督训练，采用多帧验证方法和成本敏感损失来促进无监督学习，取得了与完全监督追踪器相当的基准精度，同时展示了利用未标记或弱标记数据进一步提高跟踪精度的潜力。