Skip to main content
SpringerLink
Log in

Keypoint Context Aggregation for Human Pose Estimation

  • Conference paper
  • First Online:
Image and Graphics (ICIG 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12889))

Included in the following conference series:

  • 1651 Accesses

Abstract

Human pose estimation has drawn much attention recently, but it remains challenging due to the deformation of human joints, the occlusion between limbs, etc. And more discriminative feature representations will bring more accurate prediction results. In this paper, we explore the importance of aggregating keypoint contextual information to strengthen the feature map representations in human pose estimation. Motivated by the fact that each keypoint is characterized by its relative contextual keypoints, we devise a simple yet effective approach, namely Keypoint Context Aggregation Module, that aggregates informative keypoint contexts for better keypoint localization. Specifically, first we obtain a rough localization result, which can be considered as soft keypoint areas. Based on these soft areas, keypoint contexts are purposefully aggregated for feature representation strengthening. Experiments show that the proposed Keypoint Context Aggregation Module can be used in various backbones to boost the performance and our best model achieves a state-of-the-art of 75.8% AP on MSCOCO test-dev split.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cai, Y., et al.: Learning delicate local representations for multi-person pose estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 455–472. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58580-8_27

    Chapter  Google Scholar 

  2. Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: CVPR, pp. 7291–7299 (2017)

    Google Scholar 

  3. Carreira, J., Agrawal, P., Fragkiadaki, K., Malik, J.: Human pose estimation with iterative error feedback. In: CVPR, pp. 4733–4742 (2016)

    Google Scholar 

  4. Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. In: CVPR, pp. 7103–7112 (2018)

    Google Scholar 

  5. Cheng, B., Xiao, B., Wang, J., Shi, H., Huang, T.S., Zhang, L.: Higherhrnet: scale-aware representation learning for bottom-up human pose estimation. In: CVPR, pp. 5386–5395 (2020)

    Google Scholar 

  6. Chu, X., Yang, W., Ouyang, W., Ma, C., Yuille, A.L., Wang, X.: Multi-context attention for human pose estimation. In: CVPR, pp. 1831–1840 (2017)

    Google Scholar 

  7. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: ICCV, pp. 2961–2969 (2017)

    Google Scholar 

  8. Huang, J., Zhu, Z., Guo, F., Huang, G.: The devil is in the details: delving into unbiased data processing for human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5700–5709 (2020)

    Google Scholar 

  9. Iqbal, U., Gall, J.: Multi-person pose estimation with local joint-to-person associations. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 627–642. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_44

    Chapter  Google Scholar 

  10. Jin, S., et al.: Differentiable hierarchical graph grouping for multi-person pose estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12352, pp. 718–734. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58571-6_42

    Chapter  Google Scholar 

  11. Jin, S., et al.: Whole-body human pose estimation in the wild. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 196–214. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_12

    Chapter  Google Scholar 

  12. Kingma, D.P., Ba, J.L.: Adam: a method for stochastic gradient descent. In: ICLR, pp. 1–15 (2015)

    Google Scholar 

  13. Kocabas, M., Karagoz, S., Akbas, E.: Multiposenet: fast multi-person pose estimation using pose residual network. In: ECCV, pp. 417–433 (2018)

    Google Scholar 

  14. Kreiss, S., Bertoni, L., Alahi, A.: Pifpaf: composite fields for human pose estimation. In: CVPR, pp. 11977–11986 (2019)

    Google Scholar 

  15. Li, W., et al.: Rethinking on multi-stage networks for human pose estimation. arXiv preprint arXiv:1901.00148 (2019)

  16. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  17. Luvizon, D.C., Tabia, H., Picard, D.: Human pose regression by combining indirect part detection and contextual information. Comput. Graph. 85, 15–22 (2019)

    Article  Google Scholar 

  18. Moon, G., Chang, J.Y., Lee, K.M.: Posefix: model-agnostic general human pose refinement network. In: CVPR, pp. 7773–7781 (2019)

    Google Scholar 

  19. Newell, A., Huang, Z., Deng, J.: Associative embedding: end-to-end learning for joint detection and grouping. In: NeurIPS, pp. 2277–2287 (2017)

    Google Scholar 

  20. Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29

    Chapter  Google Scholar 

  21. Papandreou, G., Zhu, T., Chen, L.C., Gidaris, S., Tompson, J., Murphy, K.: Personlab: person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model. In: ECCV, pp. 269–286 (2018)

    Google Scholar 

  22. Papandreou, G., et al.: Towards accurate multi-person pose estimation in the wild. In: CVPR, pp. 4903–4911 (2017)

    Google Scholar 

  23. Pishchulin, L., et al.: Deepcut: joint subset partition and labeling for multi person pose estimation. In: CVPR, pp. 4929–4937 (2016)

    Google Scholar 

  24. Qiu, L., et al.: Peeking into occluded joints: a novel framework for crowd pose estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12364, pp. 488–504. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58529-7_29

    Chapter  Google Scholar 

  25. Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: CVPR, pp. 5693–5703 (2019)

    Google Scholar 

  26. Umer, R., Doering, A., Leibe, B., Gall, J.: Self-supervised keypoint correspondences for multi-person pose estimation and tracking in videos. arXiv preprint arXiv:2004.12652 (2020)

  27. Wang, J., Long, X., Gao, Y., Ding, E., Wen, S.: Graph-PCNN: two stage human pose estimation with graph pose refinement. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 492–508. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_29

    Chapter  Google Scholar 

  28. Wang, Z., et al.: Mscoco keypoints challenge 2018. In: Joint Recognition Challenge Workshop at ECCV (2018)

    Google Scholar 

  29. Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. In: ECCV, pp. 466–481 (2018)

    Google Scholar 

  30. Zhang, F., Zhu, X., Ye, M.: Fast human pose estimation. In: CVPR, pp. 3517–3526 (2019)

    Google Scholar 

  31. Zhang, H., et al.: Human pose estimation with spatial contextual information. arXiv preprint arXiv:1901.01760 (2019)

Download references

Author information

Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Wenzhu Wu, Weining Wang, Longteng Guo & Jing Liu

  2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

    Wenzhu Wu, Weining Wang, Longteng Guo & Jing Liu

Authors
  1. Wenzhu Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weining Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Longteng Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Liu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wu, W., Wang, W., Guo, L., Liu, J. (2021). Keypoint Context Aggregation for Human Pose Estimation. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87358-5_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87357-8

  • Online ISBN: 978-3-030-87358-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation