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- W3127178541 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 efficient 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." @default.
- W3127178541 created "2021-02-15" @default.
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- W3127178541 date "2020-01-01" @default.
- W3127178541 modified "2023-10-12" @default.
- W3127178541 title "RPT: Learning Point Set Representation for Siamese Visual Tracking" @default.
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- W3127178541 doi "https://doi.org/10.1007/978-3-030-68238-5_43" @default.
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