title: Tracking Anything in High Quality
summary: Visual object tracking is a fundamental video task in computer vision.
  Recently, the notably increasing power of perception algorithms allows the
  unification of single/multiobject and box/mask-based tracking. Among them, the
  Segment Anything Model (SAM) attracts much attention. In this report, we
  propose HQTrack, a framework for High Quality Tracking anything in videos.
  HQTrack mainly consists of a video multi-object segmenter (VMOS) and a mask
  refiner (MR). Given the object to be tracked in the initial frame of a video,
  VMOS propagates the object masks to the current frame. The mask results at this
  stage are not accurate enough since VMOS is trained on several closeset video
  object segmentation (VOS) datasets, which has limited ability to generalize to
  complex and corner scenes. To further improve the quality of tracking masks, a
  pretrained MR model is employed to refine the tracking results. As a compelling
  testament to the effectiveness of our paradigm, without employing any tricks
  such as test-time data augmentations and model ensemble, HQTrack ranks the 2nd
  place in the Visual Object Tracking and Segmentation (VOTS2023) challenge. Code
  and models are available at https://github.com/jiawen-zhu/HQTrack.
paper_authors._id: 64c24bae28cc6b6edf510f0d
paper_authors.name: Jiawen Zhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-27T14:23:27.168Z
paper_authors._id: 64c26e06400274d129a10c7d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/LSDuAkXGoydhLyPxDFcg2.jpeg
paper_authors.fullname: Jiawen Zhu
paper_authors.type: user
paper_authors.user: JiaWen-Z
paper_authors._id: 64c24bae28cc6b6edf510f0e
paper_authors.name: Zhenyu Chen
paper_authors._id: 64c24bae28cc6b6edf510f0f
paper_authors.name: Zeqi Hao
paper_authors._id: 64c24bae28cc6b6edf510f10
paper_authors.name: Shijie Chang
paper_authors._id: 64c24bae28cc6b6edf510f11
paper_authors.name: Lu Zhang
paper_authors._id: 64c24bae28cc6b6edf510f12
paper_authors.name: Dong Wang
paper_authors._id: 64c24bae28cc6b6edf510f13
paper_authors.name: Huchuan Lu
paper_authors._id: 64c24bae28cc6b6edf510f14
paper_authors.name: Bin Luo
paper_authors._id: 64c24bae28cc6b6edf510f15
paper_authors.name: Jun-Yan He
paper_authors._id: 64c24bae28cc6b6edf510f16
paper_authors.name: Jin-Peng Lan
paper_authors._id: 64c24bae28cc6b6edf510f17
paper_authors.name: Hanyuan Chen
paper_authors._id: 64c24bae28cc6b6edf510f18
paper_authors.name: Chenyang Li
paper_title: Tracking Anything in High Quality
paper_summary: Visual object tracking is a fundamental video task in computer vision.
  Recently, the notably increasing power of perception algorithms allows the
  unification of single/multiobject and box/mask-based tracking. Among them, the
  Segment Anything Model (SAM) attracts much attention. In this report, we
  propose HQTrack, a framework for High Quality Tracking anything in videos.
  HQTrack mainly consists of a video multi-object segmenter (VMOS) and a mask
  refiner (MR). Given the object to be tracked in the initial frame of a video,
  VMOS propagates the object masks to the current frame. The mask results at this
  stage are not accurate enough since VMOS is trained on several closeset video
  object segmentation (VOS) datasets, which has limited ability to generalize to
  complex and corner scenes. To further improve the quality of tracking masks, a
  pretrained MR model is employed to refine the tracking results. As a compelling
  testament to the effectiveness of our paradigm, without employing any tricks
  such as test-time data augmentations and model ensemble, HQTrack ranks the 2nd
  place in the Visual Object Tracking and Segmentation (VOTS2023) challenge. Code
  and models are available at https://github.com/jiawen-zhu/HQTrack.
paper_ai_keywords: Visual Object Tracking Segment with single/multi-object and box/mask-based tracking Segment Anything Model (SAM) High Quality Tracking (HQTrack) Video multi-object segmenter (VMOS) Mask refiner (MR) Close-set video object segmentation (VOS) Test-time data augmentations Model ensemble Visual Object Tracking and Segmentation (VOTS2023) challenge