HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition

  • Hossein Rahmani
  • Arif Mahmood
  • Du Q Huynh
  • Ajmal Mian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)


Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which change significantly with viewpoint. In contrast, we directly process the pointclouds and propose a new technique for action recognition which is more robust to noise, action speed and viewpoint variations. Our technique consists of a novel descriptor and keypoint detection algorithm. The proposed descriptor is extracted at a point by encoding the Histogram of Oriented Principal Components (HOPC) within an adaptive spatio-temporal support volume around that point. Based on this descriptor, we present a novel method to detect Spatio-Temporal Key-Points (STKPs) in 3D pointcloud sequences. Experimental results show that the proposed descriptor and STKP detector outperform state-of-the-art algorithms on three benchmark human activity datasets. We also introduce a new multiview public dataset and show the robustness of our proposed method to viewpoint variations.


Spatio-temporal keypoints multiview action dataset 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    UWA3D Multiview Activity dataset and Histogram of Oriented Principal Components Matlab code (2014),
  2. 2.
    Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: ICCV (2005)Google Scholar
  3. 3.
    Campbell, L., Bobick, A.: Recognition of human body motion using phase space constraints. In: ICCV (1995)Google Scholar
  4. 4.
    Cheng, Z., Qin, L., Ye, Y., Huang, Q., Tian, Q.: Human daily action analysis with multi-view and color-depth data. In: ECCVW (2012)Google Scholar
  5. 5.
    Darrell, T., Essa, I., Pentland, A.: Task-specific gesture analysis in real-time using interpolated views. PAMI (1996)Google Scholar
  6. 6.
    Dollár, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: ICCV (2005)Google Scholar
  7. 7.
    Farhadi, A., Tabrizi, M.K.: Learning to recognize activities from the wrong view point. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 154–166. Springer, Heidelberg (2008)Google Scholar
  8. 8.
    Farhadi, A., Tabrizi, M.K., Endres, I., Forsyth, D.A.: A latent model of discriminative aspect. In: ICCV (2009)Google Scholar
  9. 9.
    Gavrila, D., Davis, L.: 3D model-based tracking of humans in action: a multi-view approach. In: CVPR (1996)Google Scholar
  10. 10.
    Klaeser, A., Marszalek, M., Schmid, C.: A spatio-temporal descriptor based on 3D-gradients. In: BMVC (2008)Google Scholar
  11. 11.
    Laptev, I.: On space-time interest point. IJCV (2005)Google Scholar
  12. 12.
    Li, R.: Discriminative virtual views for cross-view action recognition. In: CVPR (2012)Google Scholar
  13. 13.
    Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: CVPRW (2010)Google Scholar
  14. 14.
    Liu, J., Shah, M., Kuipersy, B., Savarese, S.: Cross-view action recognition via view knowledge transfer. In: CVPR (2011)Google Scholar
  15. 15.
    Lv, F., Nevatia, R.: Single view human action recognition using key pose matching and viterbi path searching. In: CVPR (2007)Google Scholar
  16. 16.
    Maji, S., Berg, A.C., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: CVPR (2008)Google Scholar
  17. 17.
    Mian, A., Bennamoun, M., Owens, R.: On the repeatability and quality of keypoints for local feature-based 3D object retrieval from cluttered scenes. IJCV (2010)Google Scholar
  18. 18.
    Mitra, N.J., Nguyen, A.: Estimating surface normals in noisy point clouds data. In: SCG (2003)Google Scholar
  19. 19.
    Oreifej, O., Liu, Z.: HON4D: histogram of oriented 4D normals for activity recognition from depth sequences. In: CVPR (2013)Google Scholar
  20. 20.
    Parameswaran, V., Chellappa, R.: View invariance for human action recognition. IJCV (2006)Google Scholar
  21. 21.
    Rahmani, H., Mahmood, A., Huynh, D.Q., Mian, A.: Real time human action recognition using histograms of depth gradients and random decision forests. In: WACV (2014)Google Scholar
  22. 22.
    Rao, C., Yilmaz, A., Shah, M.: View-invariant representation and recognition of actions. IJCV (2002)Google Scholar
  23. 23.
    Seitz, S., Dyer, C.: View-invariant analysis of cyclic motion. IJCV (1997)Google Scholar
  24. 24.
    Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: CVPR (2011)Google Scholar
  25. 25.
    Syeda-Mahmood, T., Vasilescu, A., Sethi, S.: Recognizing action events from multiple viewpoints. In: IEEE Workshop on Detection and Recognition of Events in Video (2001)Google Scholar
  26. 26.
    Syeda-Mahmood, T., Vasilescu, A., Sethi, S.: Action recognition from arbitrary views using 3D exemplars. In: ICCV (2007)Google Scholar
  27. 27.
    Tang, S., Wang, X., Lv, X., Han, T.X., Keller, J., He, Z., Skubic, M., Lao, S.: Histogram of oriented normal vectors for object recognition with a depth sensor. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part II. LNCS, vol. 7725, pp. 525–538. Springer, Heidelberg (2013)Google Scholar
  28. 28.
    Timbari, F., Stefano, L.D.: Performance evaluation of 3D keypoint detectors. IJCV (2013)Google Scholar
  29. 29.
    Vieira, A.W., Nascimento, E., Oliveira, G., Liu, Z., Campos, M.: STOP: space-time occupancy patterns for 3D action recognition from depth map sequences. In: CIARP (2012)Google Scholar
  30. 30.
    Wang, H., Klaser, A., Schmid, C., Liu, C.L.: Action recognition by dense trajectories. In: CVPR (2011)Google Scholar
  31. 31.
    Wang, J., Liu, Z., Chorowski, J., Chen, Z., Wu, Y.: Robust 3D action recognition with random occupancy patterns. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 872–885. Springer, Heidelberg (2012)Google Scholar
  32. 32.
    Wang, J., Liu, Z., Wu, Y., Yuan, J.: Mining actionlet ensemble for action recognition with depth cameras. In: CVPR (2012)Google Scholar
  33. 33.
    Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes. In: CVIU (2006)Google Scholar
  34. 34.
    Wu, S., Oreifej, O., Shah, M.: Action recognition in videos acquired by a moving camera using motion decomposition of lagrangian particle trajectories. In: ICCV (2011)Google Scholar
  35. 35.
    Xia, L., Aggarwal, J.: Spatio-temporal depth cuboid similarity feature for activity recongition using depth camera. In: CVPR (2013)Google Scholar
  36. 36.
    Xia, L., Chen, C.C., Aggarwal, J.K.: View invariant human action recognition using histograms of 3D joints. In: CVPRW (2012)Google Scholar
  37. 37.
    Yang, X., Tian, Y.: EigenJoints-based action recognition using naive bayes nearest neighbor. In: CVPRW (2012)Google Scholar
  38. 38.
    Yang, X., Zhang, C., Tian, Y.: Recognizing actions using depth motion maps-based histograms of oriented gradients. In: ACM ICM (2012)Google Scholar
  39. 39.
    Yilmaz, A., Shah, M.: Action sketch: a novel action representation. In: CVPR (2005)Google Scholar
  40. 40.
    Zanfir, M., Leordeanu, M., Sminchisescu, C.: The moving pose: an efficient 3D kinematics descriptor for low-latency action recognition and detection. In: ICCV (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hossein Rahmani
    • 1
  • Arif Mahmood
    • 1
  • Du Q Huynh
    • 1
  • Ajmal Mian
    • 1
  1. 1.Computer Science and Software EngineeringThe University of Western AustraliaCrawleyAustralia

Personalised recommendations