Advertisement

Human Motion Analysis with Deep Metric Learning

  • Huseyin CoskunEmail author
  • David Joseph Tan
  • Sailesh Conjeti
  • Nassir Navab
  • Federico Tombari
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11218)

Abstract

Effectively measuring the similarity between two human motions is necessary for several computer vision tasks such as gait analysis, person identification and action retrieval. Nevertheless, we believe that traditional approaches such as L2 distance or Dynamic Time Warping based on hand-crafted local pose metrics fail to appropriately capture the semantic relationship across motions and, as such, are not suitable for being employed as metrics within these tasks. This work addresses this limitation by means of a triplet-based deep metric learning specifically tailored to deal with human motion data, in particular with the problem of varying input size and computationally expensive hard negative mining due to motion pair alignment. Specifically, we propose (1) a novel metric learning objective based on a triplet architecture and Maximum Mean Discrepancy; as well as, (2) a novel deep architecture based on attentive recurrent neural networks. One benefit of our objective function is that it enforces a better separation within the learned embedding space of the different motion categories by means of the associated distribution moments. At the same time, our attentive recurrent neural network allows processing varying input sizes to a fixed size of embedding while learning to focus on those motion parts that are semantically distinctive. Our experiments on two different datasets demonstrate significant improvements over conventional human motion metrics.

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

References

  1. 1.
    Carnegie mellon university - CMU graphics lab - motion capture library (2010). http://mocap.cs.cmu.edu/. Accessed 03 Nov 2018
  2. 2.
    Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/. Software available from tensorflow.org
  3. 3.
    Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. CoRR abs/1607.06450 (2016). http://arxiv.org/abs/1607.06450
  4. 4.
    Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, Seattle, WA, vol. 10, pp. 359–370 (1994)Google Scholar
  5. 5.
    Che, Z., He, X., Xu, K., Liu, Y.: DECADE: a deep metric learning model for multivariate time series (2017)Google Scholar
  6. 6.
    Chen, C., Zhuang, Y., Nie, F., Yang, Y., Wu, F., Xiao, J.: Learning a 3D human pose distance metric from geometric pose descriptor. IEEE Trans. Vis. Comput. Graph. 17(11), 1676–1689 (2011)CrossRefGoogle Scholar
  7. 7.
    Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 539–546. IEEE (2005)Google Scholar
  8. 8.
    Chu, X., Yang, W., Ouyang, W., Ma, C., Yuille, A.L., Wang, X.: Multi-context attention for human pose estimation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017Google Scholar
  9. 9.
    Cuturi, M., Vert, J.P., Birkenes, O., Matsui, T.: A kernel for time series based on global alignments. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2007, vol. 2, pp. II–413. IEEE (2007)Google Scholar
  10. 10.
    Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 209–216. ACM (2007)Google Scholar
  11. 11.
    Eddy, S.R.: Hidden markov models. Curr. Opin. Struct. Biol. 6(3), 361–365 (1996)CrossRefGoogle Scholar
  12. 12.
    Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6645–6649. IEEE (2013)Google Scholar
  13. 13.
    Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2017)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A.: A kernel two-sample test. J. Mach. Learn. Res. 13, 723–773 (2012)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1735–1742. IEEE (2006)Google Scholar
  16. 16.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  17. 17.
    Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6M: large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Patt. Anal. Mach. Intell. 36(7), 1325–1339 (2014)CrossRefGoogle Scholar
  18. 18.
    Keogh, E.J., Pazzani, M.J.: Derivative dynamic time warping. In: Proceedings of the 2001 SIAM International Conference on Data Mining, pp. 1–11. SIAM (2001)Google Scholar
  19. 19.
    Laurent, C., Pereyra, G., Brakel, P., Zhang, Y., Bengio, Y.: Batch normalized recurrent neural networks. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2657–2661. IEEE (2016)Google Scholar
  20. 20.
    Li, Y., Swersky, K., Zemel, R.: Generative moment matching networks. In: Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), pp. 1718–1727 (2015)Google Scholar
  21. 21.
    Lin, Z., et al.: A structured self-attentive sentence embedding. In: Proceedings of International Conference on Learning Representations (ICLR) (2017)Google Scholar
  22. 22.
    López-Méndez, A., Gall, J., Casas, J.R., Van Gool, L.J.: Metric learning from poses for temporal clustering of human motion. In: BMVC, pp. 1–12 (2012)Google Scholar
  23. 23.
    Martinez, J., Black, M.J., Romero, J.: On human motion prediction using recurrent neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017Google Scholar
  24. 24.
    Mehta, D., et al.: VNect: real-time 3D human pose estimation with a single RGB camera. ACM Trans. Graph. (TOG) 36(4), 44 (2017)CrossRefGoogle Scholar
  25. 25.
    Mei, J., Liu, M., Wang, Y.F., Gao, H.: Learning a mahalanobis distance-based dynamic time warping measure for multivariate time series classification. IEEE Trans. Cybern. 46(6), 1363–1374 (2016)CrossRefGoogle Scholar
  26. 26.
    Mishchuk, A., Mishkin, D., Radenovic, F., Matas, J.: Working hard to know your neighbor’s margins: local descriptor learning loss. In: Proceedings Conference on Neural Information Processing Systems (NIPS), December 2017Google Scholar
  27. 27.
    Movshovitz-Attias, Y., Toshev, A., Leung, T.K., Ioffe, S., Singh, S.: No fuss distance metric learning using proxies. In: The IEEE International Conference on Computer Vision (ICCV), October 2017Google Scholar
  28. 28.
    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_29CrossRefGoogle Scholar
  29. 29.
    Pei, W., Tax, D.M., van der Maaten, L.: Modeling time series similarity with siamese recurrent networks. CoRR abs/1603.04713 (2016)Google Scholar
  30. 30.
    Ratanamahatana, C.A., Keogh, E.: Making time-series classification more accurate using learned constraints. In: SIAM (2004)Google Scholar
  31. 31.
    Rippel, O., Paluri, M., Dollar, P., Bourdev, L.: Metric learning with adaptive density discrimination. In: International Conference on Learning Representations (2016)Google Scholar
  32. 32.
    Roweis, S., Hinton, G., Salakhutdinov, R.: Neighbourhood component analysis. Adv. Neural Inf. Process. Syst. (NIPS) 17, 513–520 (2004)Google Scholar
  33. 33.
    Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)Google Scholar
  34. 34.
    Schultz, M., Joachims, T.: Learning a distance metric from relative comparisons. In: Advances in Neural Information Processing Systems, pp. 41–48 (2004)Google Scholar
  35. 35.
    Sohn, K.: Improved deep metric learning with multi-class n-pair loss objective. In: Advances in Neural Information Processing Systems, pp. 1857–1865 (2016)Google Scholar
  36. 36.
    Song, H.O., Xiang, Y., Jegelka, S., Savarese, S.: Deep metric learning via lifted structured feature embedding. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4004–4012. IEEE (2016)Google Scholar
  37. 37.
    Sun, X., Shang, J., Liang, S., Wei, Y.: Compositional human pose regression. In: The IEEE International Conference on Computer Vision (ICCV), vol. 2 (2017)Google Scholar
  38. 38.
    Sutherland, D.J., et al.: Generative models and model criticism via optimized maximum mean discrepancy. In: Proceedings of the 32nd International Conference on Machine Learning (ICML 2017) (2017)Google Scholar
  39. 39.
    Taylor, G.W., Hinton, G.E., Roweis, S.T.: Modeling human motion using binary latent variables. In: Advances in Neural Information Processing Systems, pp. 1345–1352 (2007)Google Scholar
  40. 40.
    Tian, B.F.Y., Wu, F.: L2-Net: deep learning of discriminative patch descriptor in Euclidean space. In: Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2 (2017)Google Scholar
  41. 41.
    Trigeorgis, G., Nicolaou, M.A., Schuller, B.W., Zafeiriou, S.: Deep canonical time warping for simultaneous alignment and representation learning of sequences. IEEE Trans. Patt. Anal. Mach. Intell. 5, 1128–1138 (2018)CrossRefGoogle Scholar
  42. 42.
    Vintsyuk, T.K.: Speech discrimination by dynamic programming. Cybernetics 4(1), 52–57 (1968)MathSciNetCrossRefGoogle Scholar
  43. 43.
    Yang, W., Li, S., Ouyang, W., Li, H., Wang, X.: Learning feature pyramids for human pose estimation. In: The IEEE International Conference on Computer Vision (ICCV), October 2017Google Scholar
  44. 44.
    Yin, X., Chen, Q.: Deep metric learning autoencoder for nonlinear temporal alignment of human motion. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 2160–2166. IEEE (2016)Google Scholar
  45. 45.
    Zhang, X., Yu, F.X., Kumar, S., Chang, S.F.: Learning spread-out local feature descriptors. In: The IEEE International Conference on Computer Vision (ICCV), October 2017Google Scholar
  46. 46.
    Zheng, Y., Liu, Q., Chen, E., Zhao, J.L., He, L., Lv, G.: Convolutional nonlinear neighbourhood components analysis for time series classification. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9078, pp. 534–546. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-18032-8_42CrossRefGoogle Scholar
  47. 47.
    Zhou, F., Torre, F.: Canonical time warping for alignment of human behavior. In: Advances in Neural Information Processing Systems, pp. 2286–2294 (2009)Google Scholar
  48. 48.
    Zhou, F., De la Torre, F.: Generalized canonical time warping. IEEE Trans. Patt. Anal. Mach. Intell. 38(2), 279–294 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Huseyin Coskun
    • 1
    Email author
  • David Joseph Tan
    • 1
    • 2
  • Sailesh Conjeti
    • 1
  • Nassir Navab
    • 1
    • 2
  • Federico Tombari
    • 1
    • 2
  1. 1.Technische Universität MünchenMunichGermany
  2. 2.Pointu3D GmbHMunichGermany

Personalised recommendations

Cite paper

Buy options