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Movement Pattern Histogram for Action Recognition and Retrieval

  • Arridhana Ciptadi
  • Matthew S. Goodwin
  • James M. Rehg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)

Abstract

We present a novel action representation based on encoding the global temporal movement of an action. We represent an action as a set of movement pattern histograms that encode the global temporal dynamics of an action. Our key observation is that temporal dynamics of an action are robust to variations in appearance and viewpoint changes, making it useful for action recognition and retrieval. We pose the problem of computing similarity between action representations as a maximum matching problem in a bipartite graph. We demonstrate the effectiveness of our method for cross-view action recognition on the IXMAS dataset. We also show how our representation complements existing bag-of-features representations on the UCF50 dataset. Finally we show the power of our representation for action retrieval on a new real-world dataset containing repetitive motor movements emitted by children with autism in an unconstrained classroom setting.

Keywords

Action Recognition Transfer Learning Viewpoint Change Fisher Vector Background Motion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Albinali, F., Goodwin, M.S., Intille, S.S.: Recognizing stereotypical motor movements in the laboratory and classroom: A case study with children on the aautism spectrum. In: UbiComp (2009)Google Scholar
  2. 2.
    American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders DSM-IV-TR, 4th edn. Text Revision. American Psychiatric Pub. (2000)Google Scholar
  3. 3.
    Cao, L., Ji, R., Gao, Y., Liu, W., Tian, Q.: Mining spatiotemporal video patterns towards robust action retrieval. Neurocomputing (2012)Google Scholar
  4. 4.
    Chaudhry, R., Ravichandran, A., Hager, G., Vidal, R.: Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions. In: CVPR (2009)Google Scholar
  5. 5.
    Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior Recognition via Sparse Spatio-Temporal Features. ICCV-VS PETS (2005)Google Scholar
  6. 6.
    Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as Space-Time Shapes. PAMI (2007)Google Scholar
  7. 7.
    Grundmann, M., Kwatra, V., Essa, I.: Auto-directed video stabilization with robust l1 optimal camera paths. In: CVPR (2011)Google Scholar
  8. 8.
    Jain, M., Jégou, H., Bouthemy, P., et al.: Better exploiting motion for better action recognition. In: CVPR (2013)Google Scholar
  9. 9.
    Johansson, G.: Visual Perception of Biological Motion and a Model for Its Analysis. Attention, Perception, & Psychophysics 14(2), 201–211 (1973)MathSciNetGoogle Scholar
  10. 10.
    Junejo, I., Dexter, E., Laptev, I., Pérez, P.: View-Independent Action Recognition from Temporal Self-Similarities. PAMI (2010)Google Scholar
  11. 11.
    Ke, Y., Sukthankar, R., Hebert, M.: Volumetric features for video event detection. IJCV (2010)Google Scholar
  12. 12.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foreground–background segmentation using codebook model. Real-time imaging 11(3), 172–185 (2005)CrossRefGoogle Scholar
  13. 13.
    Kliper-Gross, O., Gurovich, Y., Hassner, T., Wolf, L.: Motion interchange patterns for action recognition in unconstrained videos. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 256–269. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning Realistic Human Actions from Movies. In: CVPR (2008)Google Scholar
  15. 15.
    Li, B., Camps, O.I., Sznaier, M.: Cross-view activity recognition using hankelets. In: CVPR (2012)Google Scholar
  16. 16.
    Li, R., Zickler, T.: Discriminative Virtual Views for Cross-View Action Recognition. In: CVPR (2012)Google Scholar
  17. 17.
    Liu, J., Shah, M., Kuipers, B., Savarese, S.: Cross-View Action Recognition via View Knowledge Transfer. In: CVPR (2011)Google Scholar
  18. 18.
    Messing, R., Pal, C., Kautz, H.: Activity Recognition Using the Velocity Histories of Tracked Keypoints. In: ICCV (2009)Google Scholar
  19. 19.
    Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing 28(6), 976–990 (2010)CrossRefGoogle Scholar
  21. 21.
    Reddy, K.K., Shah, M.: Recognizing 50 human action categories of web videos. Machine Vision and Applications (2012)Google Scholar
  22. 22.
    Sadanand, S., Corso, J.J.: Action bank: A high-level representation of activity in video. In: CVPR (2012)Google Scholar
  23. 23.
    Shi, J., Tomasi, C.: Good features to track. In: CVPR (1994)Google Scholar
  24. 24.
    Solmaz, B., Assari, S.M., Shah, M.: Classifying web videos using a global video descriptor. Machine Vision and Applications (2012)Google Scholar
  25. 25.
    Tran, D., Sorokin, A.: Human Activity Recognition with Metric Learning. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 548–561. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  26. 26.
    Wang, H., Kläser, A., Schmid, C., Liu, C.L.: Dense trajectories and motion boundary descriptors for action recognition. In: IJCV (2013)Google Scholar
  27. 27.
    Weinland, D., Boyer, E., Ronfard, R.: Action Recognition from Arbitrary Views using 3D Exemplars. IJCV (2007)Google Scholar
  28. 28.
    Werlberger, M., Trobin, W., Pock, T., Wedel, A., Cremers, D., Bischof, H.: Anisotropic Huber-L1 optical flow. BMVC (2009)Google Scholar
  29. 29.
    Yuan, C., Li, X., Hu, W., Ling, H., Maybank, S.: 3d r transform on spatio-temporal interest points for action recognition. In: CVPR (2013)Google Scholar
  30. 30.
    Zhang, Z., Wang, C., Xiao, B., Zhou, W., Liu, S., Shi, C.: Cross-view action recognition via a continuous virtual path. In: CVPR (2013)Google Scholar
  31. 31.
    Zhou, F., de la Torre, F.: Canonical Time Warping for Alignment of Human Behavior. NIPS (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Arridhana Ciptadi
    • 1
  • Matthew S. Goodwin
    • 2
  • James M. Rehg
    • 1
  1. 1.College of ComputingGeorgia Institute of TechnologyUSA
  2. 2.Department of Health SciencesNortheastern UniversityUSA

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