ProTracker: Probabilistic Integration for Robust and Accurate Point
  Tracking
Authors: name: Tingyang Zhang, name: Chen Wang, name: Zhiyang Dou, name: Qingzhe Gao, name: Jiahui Lei, name: Baoquan Chen, name: Lingjie Liu

In this paper, we propose ProTracker, a novel framework for robust and
accurate long-term dense tracking of arbitrary points in videos. The key idea
of our method is incorporating probabilistic integration to refine multiple
predictions from both optical flow and semantic features for robust short-term
and long-term tracking. Specifically, we integrate optical flow estimations in
a probabilistic manner, producing smooth and accurate trajectories by
maximizing the likelihood of each prediction. To effectively re-localize
challenging points that disappear and reappear due to occlusion, we further
incorporate long-term feature correspondence into our flow predictions for
continuous trajectory generation. Extensive experiments show that ProTracker
achieves the state-of-the-art performance among unsupervised and
self-supervised approaches, and even outperforms supervised methods on several
benchmarks. Our code and model will be publicly available upon publication.