Tracking Anything in High Quality
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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Jiawen Zhu
claimed_verified
2023-07-27T14:23:27.168Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/LSDuAkXGoydhLyPxDFcg2.jpeg
Jiawen Zhu
user
JiaWen-Z
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Zhenyu Chen
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Zeqi Hao
64c24bae28cc6b6edf510f10
Shijie Chang
64c24bae28cc6b6edf510f11
Lu Zhang
64c24bae28cc6b6edf510f12
Dong Wang
64c24bae28cc6b6edf510f13
Huchuan Lu
64c24bae28cc6b6edf510f14
Bin Luo
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Jun-Yan He
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Jin-Peng Lan
64c24bae28cc6b6edf510f17
Hanyuan Chen
64c24bae28cc6b6edf510f18
Chenyang Li
Tracking Anything in High Quality
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.
HQTrack is a high-quality tracking framework using a video multi-object segmenter and a mask refiner to achieve competitive results in the VOTS2023 challenge.
video multi-object segmenter mask refiner Segment Anything Model visual object segmentation VOTS2023 challenge