DoubleHigherNet : coarse-to-fine precise heatmap bottom-up dynamic pose computer intelligent estimation
Peng, Yiheng and Jiang, Zhichun (2021) DoubleHigherNet : coarse-to-fine precise heatmap bottom-up dynamic pose computer intelligent estimation. Journal of Physics: Conference Series, 2033 (1). 012068. ISSN 1742-6588 (https://doi.org/10.1088/1742-6596/2033/1/012068)
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Abstract
Accurate keypoint positioning is necessary for bottom-up multi-person pose estimation methods to handle scale variation and crowdedness. In this paper, we present DoubleHigherNet: a novel network learning scale-aware and precise heatmap representation for bottom-up process using double high-resolution feature pyramids and coarse-to-fine training. The two feature pyramids in DoubleHigherNet consists of 1/4 resolution feature and higher-resolution (1/2) maps generated by attention fusion blocks and transposed convolutions. Benefited by the training strategy, muti-resoltion and coarse-fine heatmap aggregation, the proposed approach is able to predict keypoints more accurately so as to perform better on difficult crowded scenes. DoubleHigherNetw32 achieves competitive result on CrowdPose-test, surpassing all the top-down methods and bottom-up SOTA HigherHRNet-w32 (which possesses similar number of params with DoubleHigherNet-w32).
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Item type: Article ID code: 78974 Dates: DateEvent5 October 2021Published13 June 2021Published Online30 April 2021AcceptedSubjects: Science > Physics Department: UNSPECIFIED Depositing user: Pure Administrator Date deposited: 20 Dec 2021 10:57 Last modified: 11 Nov 2024 13:20 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/78974
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