title: ProTracker: Probabilistic Integration for Robust and Accurate Point
    Tracking
summary: 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.
paper_authors._id: 677dddd3035e504570000c7c
paper_authors.name: Tingyang Zhang
paper_authors._id: 677dddd3035e504570000c7d
paper_authors.name: Chen Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-08T09:45:00.720Z
paper_authors._id: 62e9d5af6687b60b9c01240a
paper_authors.avatarUrl: /avatars/3983be5662aa28aefcb18deaf08d7cb1.svg
paper_authors.fullname: Chen Wang
paper_authors.type: user
paper_authors.user: chenwang
paper_authors._id: 677dddd3035e504570000c7e
paper_authors.name: Zhiyang Dou
paper_authors._id: 677dddd3035e504570000c7f
paper_authors.name: Qingzhe Gao
paper_authors._id: 677dddd3035e504570000c80
paper_authors.name: Jiahui Lei
paper_authors._id: 677dddd3035e504570000c81
paper_authors.name: Baoquan Chen
paper_authors._id: 677dddd3035e504570000c82
paper_authors.name: Lingjie Liu
paper_title: ProTracker: Probabilistic Integration for Robust and Accurate Point
    Tracking
paper_summary: 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.
paper_ai_keywords: probabilistic integration optical flow semantic features smooth and accurate trajectories likelihood of each prediction long-term feature correspondence trajectory generation unsupervised approaches self-supervised approaches supervised methods state-of-the-art performance