A Top-Down Approach to Articulated Human Pose Estimation and Tracking

  • Guanghan NingEmail author
  • Ping Liu
  • Xiaochuan Fan
  • Chi Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11130)


Both the tasks of multi-person human pose estimation and pose tracking in videos are quite challenging. Existing methods can be categorized into two groups: top-down and bottom-up approaches. In this paper, following the top-down approach, we aim to build a strong baseline system with three modules: human candidate detector, single-person pose estimator and human pose tracker. Firstly, we choose a generic object detector among state-of-the-art methods to detect human candidates. Then, cascaded pyramid network is used to estimate the corresponding human pose. Finally, we use a flow-based pose tracker to render keypoint-association across frames, i.e., assigning each human candidate a unique and temporally-consistent id, for the multi-target pose tracking purpose. We conduct extensive ablative experiments to validate various choices of models and configurations. We take part in two ECCV’18 PoseTrack challenges ( pose estimation and pose tracking.


Multi-person pose estimation Multi-person pose tracking 


  1. 1.
    Andriluka, M., et al.: PoseTrack: a benchmark for human pose estimation and tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5167–5176 (2018)Google Scholar
  2. 2.
    Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: CVPR (2017)Google Scholar
  3. 3.
    Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. In: CVPR (2018)Google Scholar
  4. 4.
    Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems, pp. 379–387 (2016)Google Scholar
  5. 5.
    Dai, J., et al.: Deformable convolutional networks. CoRR, abs/1703.06211 1(2), 3 (2017)Google Scholar
  6. 6.
    Girdhar, R., Gkioxari, G., Torresani, L., Paluri, M., Tran, D.: Detect-and-track: efficient pose estimation in videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 350–359 (2018)Google Scholar
  7. 7.
    Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)Google Scholar
  8. 8.
    Fang, H.S., Xie, S., Tai, Y.W., Lu, C.: RMPE: regional multi-person pose estimation. In: ICCV (2017)Google Scholar
  9. 9.
    He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988. IEEE (2017)Google Scholar
  10. 10.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
  11. 11.
    Insafutdinov, E., et al.: ArtTrack: articulated multi-person tracking in the wild. In: CVPR, vol. 4327. IEEE (2017)Google Scholar
  12. 12.
    Iqbal, U., Milan, A., Gall, J.: PoseTrack: joint multi-person pose estimation and tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  13. 13.
    Lin, T.Y., Dollár, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. In: CVPR, vol. 1, p. 3 (2017)Google Scholar
  14. 14.
    Newell, A., Huang, Z., Deng, J.: Associative embedding: end-to-end learning for joint detection and grouping. In: Advances in Neural Information Processing Systems, pp. 2277–2287 (2017)Google Scholar
  15. 15.
    Ning, G., Zhang, Z., He, Z.: Knowledge-guided deep fractal neural networks for human pose estimation. IEEE Trans. Multimed. 20(5), 1246–1259 (2018)CrossRefGoogle Scholar
  16. 16.
    Papandreou, G., et al.: Towards accurate multi-person pose estimation in the wild. In: CVPR, vol. 3, p. 6 (2017)Google Scholar
  17. 17.
    Shao, S., et al.: CrowdHuman: a benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018)
  18. 18.
    Xia, F., Wang, P., Chen, X., Yuille, A.L.: Joint multi-person pose estimation and semantic part segmentation. In: CVPR, vol. 2, p. 7 (2017)Google Scholar
  19. 19.
    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). Scholar
  20. 20.
    Xiu, Y., Li, J., Wang, H., Fang, Y., Lu, C.: Pose flow: efficient online pose tracking. In: BMVC (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. Silicon Valley Research CenterMountain ViewUSA

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