ProTracker: Probabilistic Integration for Robust and Accurate Point
  Tracking
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.
677dddd3035e504570000c7c
Tingyang Zhang
677dddd3035e504570000c7d
Chen Wang
claimed_verified
2025-01-08T09:45:00.720Z
62e9d5af6687b60b9c01240a
/avatars/3983be5662aa28aefcb18deaf08d7cb1.svg
Chen Wang
user
chenwang
677dddd3035e504570000c7e
Zhiyang Dou
claimed_verified
2025-06-02T13:40:44.514Z
645223fb01d7bd9555ea399a
https://cdn-avatars.huggingface.co/v1/production/uploads/645223fb01d7bd9555ea399a/fVR7XmGg6pMSRKx9sEdvT.png
Zhiyang Dou
user
frankzydou
677dddd3035e504570000c7f
Qingzhe Gao
677dddd3035e504570000c80
Jiahui Lei
677dddd3035e504570000c81
Baoquan Chen
677dddd3035e504570000c82
Lingjie Liu
ProTracker: Probabilistic Integration for Robust and Accurate Point
  Tracking
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.
ProTracker is a framework for robust long-term tracking using probabilistic integration of optical flow and semantic features, achieving superior performance in unsupervised and self-supervised settings.
probabilistic integration optical flow semantic features trajectory generation long-term feature correspondence