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Combining detailed appearance and multi-scale representation: a structure-context complementary network for human pose estimation

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Abstract

Human pose estimation is a fundamental and challenging task in the field of computer vision. Hard scenarios, such as occlusion and background confusion, set a great challenge for high-level feature representation because both detailed and multi-scale context must be correctly reasoned. In this paper, we propose a structure-context complementary network (SCC-Net) characterized by the complementarity between a pixel-wise enhanced attention mechanism and atrous convolution-based module. The proposed cross-coordinate attention bottleneck (CCAB) aims to utilize a cross-guide mechanism to promote the robustness of the existing coordinate attention module (CAM) for the background impact. As a complementary module for CCAB, waterfall residual atrous pooling (WRAP) is proposed to refine structure consistency by generating multi-scale features without the feature sparse defect of atrous-based methods. We evaluate our proposed modules and holistic SCC-Net on the COCO and MPII benchmark datasets. Ablation experiments demonstrate that our proposed modules can efficiently boost the performance of body joint detection. Competitive performance is also achieved by our holistic SCC-Net compared to other state-of-the-art methods.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61902404, 62001475, 62071472, in part by the Program for “Industrial IoT and Emergency Collaboration” Innovative Research Team in China University of Mining and Technology (CUMT) under Grant 2020ZY002, in part by the Special Fund for cultivating major projects in China University of Mining and Technology (CUMT) under Grand 2020-10991, in part by the Graduate Research and Innovation Projects of Jiangsu Province (KYCX21_2241), in part by China Scholarship Council under Grand 202106420091, and in part by the Yulin smart energy big data application joint Key Laboratory under Grant 202100208-01.

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Authors and Affiliations

  1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China

    Kaiwen Dong & Yanjing Sun

  2. Xuzhou Engineering Research Center of Intelligent Industry Safety and Emergency Collaboration, Xuzhou, 221116, China

    Kaiwen Dong & Yanjing Sun

  3. Sinosteel Maanshan General Institute of Mining Research Co., Ltd., Maanshan, 243000, China

    Xiaozhou Cheng

  4. School of Mines, China University of Mining and Technology, Xuzhou, 221116, China

    Xiaolin Wang

  5. Department of Communication Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Bin Wang

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  1. Kaiwen Dong
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Dong, K., Sun, Y., Cheng, X. et al. Combining detailed appearance and multi-scale representation: a structure-context complementary network for human pose estimation. Appl Intell 53, 8097–8113 (2023). https://doi.org/10.1007/s10489-022-03909-2

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