Skip to main content
SpringerLink
Log in

Occlusion-Aware Siamese Network for Human Pose Estimation

  • Conference paper
  • First Online:
Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12365))

Included in the following conference series:

Abstract

Pose estimation usually suffers from varying degrees of performance degeneration owing to occlusion. To conquer this dilemma, we propose an occlusion-aware siamese network to improve the performance. Specifically, we introduce scheme of feature erasing and reconstruction. Firstly, we utilize attention mechanism to predict the occlusion-aware attention map which is explicitly supervised and clean the feature map which is contaminated by different types of occlusions. Nevertheless, the cleaning procedure not only removes the useless information but also erases some valuable details. To overcome the defects caused by the erasing operation, we perform feature reconstruction to recover the information destroyed by occlusion and details lost in cleaning procedure. To make reconstructed features more precise and informative, we adopt siamese network equipped with OT divergence to guide the features of occluded images towards those of the un-occluded images. Algorithm is validated on MPII, LSP and COCO benchmarks and we achieve promising results.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2D human pose estimation: new benchmark and state of the art analysis. In: Proceedings of the IEEE Conference on computer Vision and Pattern Recognition, pp. 3686–3693 (2014)

    Google Scholar 

  2. Chen, L., et al.: Symmetric variational autoencoder and connections to adversarial learning. In: International Conference on Artificial Intelligence and Statistics, pp. 661–669 (2018)

    Google Scholar 

  3. Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7103–7112 (2018)

    Google Scholar 

  4. Chen, Y., Shen, C., Wei, X.S., Liu, L., Yang, J.: Adversarial PoseNet: a structure-aware convolutional network for human pose estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1212–1221 (2017)

    Google Scholar 

  5. Chou, C.J., Chien, J.T., Chen, H.T.: Self adversarial training for human pose estimation. arXiv preprint arXiv:1707.02439 (2017)

  6. Chu, X., Ouyang, W., Li, H., Wang, X.: Structured feature learning for pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4715–4723 (2016)

    Google Scholar 

  7. Chu, X., Ouyang, W., Wang, X., et al.: CRF-CNN: modeling structured information in human pose estimation. In: Advances in Neural Information Processing Systems, pp. 316–324 (2016)

    Google Scholar 

  8. Chu, X., Yang, W., Ouyang, W., Ma, C., Yuille, A.L., Wang, X.: Multi-context attention for human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1831–1840 (2017)

    Google Scholar 

  9. Genevay, A., Peyré, G., Cuturi, M.: Learning generative models with Sinkhorn divergences. arXiv preprint arXiv:1706.00292 (2017)

  10. Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., Schiele, B.: DeeperCut: a deeper, stronger, and faster multi-person pose estimation model. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 34–50. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_3

    Chapter  Google Scholar 

  11. Jain, A., Tompson, J., Andriluka, M., Taylor, G.W., Bregler, C.: Learning human pose estimation features with convolutional networks. arXiv preprint arXiv:1312.7302 (2013)

  12. Johnson, S., Everingham, M.: Clustered pose and nonlinear appearance models for human pose estimation. In: BMVC, vol. 2, p. 5. Citeseer (2010)

    Google Scholar 

  13. Ke, L., Chang, M.-C., Qi, H., Lyu, S.: Multi-scale structure-aware network for human pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 731–746. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01216-8_44

    Chapter  Google Scholar 

  14. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  15. Li, H., Dai, B., Shi, S., Ouyang, W., Wang, X.: Feature intertwiner for object detection. In: International Conference on Learning Representations (2018)

    Google Scholar 

  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. Liu, W., Chen, J., Li, C., Qian, C., Chu, X., Hu, X.: A cascaded inception of inception network with attention modulated feature fusion for human pose estimation. In: AAAI (2018)

    Google Scholar 

  18. Lu, Y., Chen, L., Saidi, A.: Optimal transport for deep joint transfer learning. arXiv preprint arXiv:1709.02995 (2017)

  19. Marras, I., Palasek, P., Patras, I.: Deep globally constrained MRFs for human pose estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3466–3475 (2017)

    Google Scholar 

  20. Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Comput. Vis. Image Underst. 81(3), 231–268 (2001)

    Article  Google Scholar 

  21. 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 

  22. Nie, X., Feng, J., Zuo, Y., Yan, S.: Human pose estimation with parsing induced learner. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2100–2108 (2018)

    Google Scholar 

  23. Ning, G., Zhang, Z., He, Z.: Knowledge-guided deep fractal neural networks for human pose estimation. IEEE Trans. Multimedia 20(5), 1246–1259 (2018)

    Article  Google Scholar 

  24. Rafi, U., Leibe, B., Gall, J., Kostrikov, I.: An efficient convolutional network for human pose estimation. In: BMVC, vol. 1, p. 2 (2016)

    Google Scholar 

  25. Salimans, T., Zhang, H., Radford, A., Metaxas, D.: Improving GANs using optimal transport. arXiv preprint arXiv:1803.05573 (2018)

  26. Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with directed graph neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7912–7921 (2019)

    Google Scholar 

  27. Su, C., Li, J., Zhang, S., Xing, J., Gao, W., Tian, Q.: Pose-driven deep convolutional model for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3960–3969 (2017)

    Google Scholar 

  28. Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5693–5703 (2019)

    Google Scholar 

  29. Tang, W., Wu, Y.: Does learning specific features for related parts help human pose estimation? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1107–1116 (2019)

    Google Scholar 

  30. Tang, W., Yu, P., Wu, Y.: Deeply learned compositional models for human pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 197–214. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_12

    Chapter  Google Scholar 

  31. Tieleman, T., Hinton, G.: Lecture 6.5-RMSprop: divide the gradient by a running average of its recent magnitude. COURSERA Neural Netw. Mach. Learn. 4(2), 26–31 (2012)

    Google Scholar 

  32. Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C.: Efficient object localization using convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 648–656 (2015)

    Google Scholar 

  33. Tompson, J.J., Jain, A., LeCun, Y., Bregler, C.: Joint training of a convolutional network and a graphical model for human pose estimation. In: Advances in Neural Information Processing Systems, pp. 1799–1807 (2014)

    Google Scholar 

  34. Toshev, A., Szegedy, C.: DeepPose: human pose estimation via deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1653–1660 (2014)

    Google Scholar 

  35. Wang, W., Xu, H., Wang, G., Wang, W., Carin, L.: An optimal transport framework for zero-shot learning. arXiv preprint arXiv:1910.09057 (2019)

  36. Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4724–4732 (2016)

    Google Scholar 

  37. Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 472–487. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_29

    Chapter  Google Scholar 

  38. Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  39. Yang, W., Li, S., Ouyang, W., Li, H., Wang, X.: Learning feature pyramids for human pose estimation. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 1290–1299. IEEE (2017)

    Google Scholar 

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

  41. Zhou, L., Chen, Y., Wang, J., Lu, H.: Progressive bi-c3d pose grammar for human pose estimation. In: AAAI, pp. 13033–13040 (2020)

    Google Scholar 

  42. Zhou, L., Chen, Y., Wang, J., Tang, M., Lu, H.: Bi-directional message passing based scanet for human pose estimation. In: 2019 IEEE International Conference on Multimedia and Expo (ICME), pp. 1048–1053. IEEE (2019)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Research and Development Projects in the Key Areas of Guangdong Province (No.2019B010153001), National Natural Science Foundation of China under Grants 61772527, 61976520 and 61806200. This work was also supported by the Technology Cooperation Project of Application Laboratory, Huawei Technologies Co., Ltd. (FA2018111061-2019SOW05).

Author information

Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Lu Zhou, Yingying Chen, Yunze Gao, Jinqiao Wang & Hanqing Lu

  2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China

    Lu Zhou, Yingying Chen, Yunze Gao, Jinqiao Wang & Hanqing Lu

  3. ObjectEye Inc., Beijing, China

    Yingying Chen

  4. NEXWISE Co., Ltd., Guangzhou, China

    Jinqiao Wang

Authors
  1. Lu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yingying Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yunze Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinqiao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hanqing Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lu Zhou .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhou, L., Chen, Y., Gao, Y., Wang, J., Lu, H. (2020). Occlusion-Aware Siamese Network for Human Pose Estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12365. Springer, Cham. https://doi.org/10.1007/978-3-030-58565-5_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58565-5_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58564-8

  • Online ISBN: 978-3-030-58565-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation