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.