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

TL;DR
HQTrack is a high-quality video object tracking framework that combines a multi-object segmenter and a mask refiner, achieving state-of-the-art results without test-time tricks.
Contribution
The paper introduces HQTrack, a novel framework integrating VMOS and a pretrained mask refiner for improved tracking accuracy in complex scenes.
Findings
Ranks 2nd in VOTS2023 challenge
Effective mask refinement without test-time tricks
Generalizes well to complex and corner scenes
Abstract
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…
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Taxonomy
TopicsVisual Attention and Saliency Detection · Video Surveillance and Tracking Methods · Advanced Neural Network Applications
MethodsAdam · 1-bit Adam
