Papers › Tracking Anything in High Quality
Tracking Anything in High Quality
Jiawen Zhu, Zhenyu Chen, Zeqi Hao, Shijie Chang, Lu Zhang, Dong Wang, Huchuan Lu, Bin Luo, Jun-Yan He, Jin-Peng Lan, Hanyuan Chen, Chenyang Li
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Video Object Segmentation | YouTube-VOS 2019 | DEVA | F-Measure (Seen) | 89.9 | #5 of 22 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | YouTube-VOS 2019 | DEVA | F-Measure (Unseen) | 89.1 | #5 of 22 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | YouTube-VOS 2019 | DEVA | FPS | 25.3 | #5 of 22 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | YouTube-VOS 2019 | DEVA | Jaccard (Seen) | 85.4 | #5 of 22 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | YouTube-VOS 2019 | DEVA | Jaccard (Unseen) | 89.9 | #5 of 22 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | YouTube-VOS 2019 | DEVA | Overall | 86.2 | #5 of 22 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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