Advertisement

The Overview of Multi-person Pose Estimation Method

  • Bingyi LiEmail author
  • Jiaqi Zou
  • Luyao Wang
  • Xiangyuan Li
  • Yue Li
  • Rongjia Lei
  • Songlin Sun
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 550)

Abstract

Research on Multi-person pose estimation is partly improved by deep learning and the computer vision. Multi-person pose estimation is expected to be involved in many applications, such as fitness training, pedestrian recognition, military training, and so on. The prospect of multi-person estimation development is promising and challenging. This paper provides a brief survey on four major multi-person pose estimation methods – DeepCut, DeeperCut, OpenPose and AlphaPose, and presents the advantages and disadvantages of these methods.

Keywords

Multi-person pose estimation DeepCut DeeperCut OpenPose AlphaPose 

Notes

Acknowledgment

This work is supported by National Natural Science Foundation of China (Project61471066) and the open project fund (No. 201600017) of the National Key Laboratory of Electromagnetic Environment, China.

References

  1. 1.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. In: IJCV (2005)Google Scholar
  2. 2.
    Andriluka, M., Roth, S., Schiele, B.: Monocular 3D pose estimation and tracking by detection. In: CVPR (2010)Google Scholar
  3. 3.
    Andriluka, M., Roth, S., Schiele, B.: Pictorial structures revisited: people detection and articulated pose estimation. In: CVPR (2009)Google Scholar
  4. 4.
    Pishchulin, L., Jain, A., Andriluka, M., Thormahlen, T., Schiele, B.: Articulated people detection and pose estimation: reshaping the future. In: CVPR (2012)Google Scholar
  5. 5.
    Gkioxari, G., Hariharan, B., Girshick, R., Malik, J.: Using kposelets for detecting people and localizing their keypoints. In: CVPR (2014)Google Scholar
  6. 6.
    Sun, M., Savarese, S.: Articulated part-based model for joint object detection and pose estimation. In: ICCV (2011)Google Scholar
  7. 7.
    Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: ECCV (2016)Google Scholar
  8. 8.
    Wei, S.-E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: CVPR (2016)Google Scholar
  9. 9.
    Ouyang, W., Chu, X., Wang, X.: Multi-source deep learning for human pose estimation. In: CVPR (2014)Google Scholar
  10. 10.
    Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P., Schiele, B.: Deepcut: joint subset partition and labeling for multi person pose estimation. In: CVPR (2016)Google Scholar
  11. 11.
    Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., Schiele, B.: Deepercut: a deeper, stronger, and faster multi-person pose estimation model. In: ECCV (2016)Google Scholar
  12. 12.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
  13. 13.
    Cao, Z., Simon, T., Wei, S., Sheikh, Y.: Real-time multi-person 2D pose estimation using part affinity fields. In: CVPR (2017)Google Scholar
  14. 14.
    Wang, H., An, W.P., Wang, X., Fang, L., Yuan, J.: Magnify-net for multi-person 2D pose estimation. In: ICME (2018)Google Scholar
  15. 15.
    Chen, X., Yang, G.: Multi-person pose estimation with LIMB detection heatmaps. In: ICIP (2018)Google Scholar
  16. 16.
    Mehta, D., Sotnychenko, O., Mueller, F., Xu, W., Sridhar, S., Pons-Moll, G., Theobalt, C.: Single-shot multi-person 3D pose estimation from monocular RGB. In: 3DV (2018)Google Scholar
  17. 17.
    Fang, H., Xie, S., Tai, Y., Lu, C.: RMPE: regional multi-person pose estimation. In: ICCV (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Bingyi Li
    • 1
    • 2
    • 3
    Email author
  • Jiaqi Zou
    • 1
    • 2
    • 3
  • Luyao Wang
    • 3
  • Xiangyuan Li
    • 4
  • Yue Li
    • 4
  • Rongjia Lei
    • 1
    • 2
    • 3
  • Songlin Sun
    • 1
    • 2
    • 3
  1. 1.National Engineering Laboratory for Mobile Network SecurityBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Key Laboratory of Trustworthy Distributed Computing and Service (BUPT)Ministry of Education, Beijing University of Posts and TelecommunicationsBeijingChina
  3. 3.School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijingChina
  4. 4.School of Computer ScienceBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations