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arXiv · 2008.03467

RPT: Learning Point Set Representation for Siamese Visual Tracking

Ziang Ma·Linyuan Wang·Haitao Zhang·Wei Lu·Jun Yin
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Abstract

While remarkable progress has been made in robust visual tracking, accurate target state estimation still remains a highly challenging problem. In this paper, we argue that this issue is closely related to the prevalent bounding box representation, which provides only a coarse spatial extent of object. Thus an effcient visual tracking framework is proposed to accurately estimate the target state with a finer representation as a set of representative points. The point set is trained to indicate the semantically and geometrically significant positions of target region, enabling more fine-grained localization and modeling of object appearance. We further propose a multi-level aggregation strategy to obtain detailed structure information by fusing hierarchical convolution layers. Extensive experiments on several challenging benchmarks including OTB2015, VOT2018, VOT2019 and GOT-10k demonstrate that our method achieves new state-of-the-art performance while running at over 20 FPS.

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Ziang Ma, Linyuan Wang, Haitao Zhang, Wei Lu, Jun Yin. 2020-08-08. RPT: Learning Point Set Representation for Siamese Visual Tracking. https://arxiv.org/abs/2008.03467

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