Exemplar-Based Human Action Recognition with Template Matching from a Stream of Motion Capture

  • Daniel LeightleyEmail author
  • Baihua Li
  • Jamie S. McPhee
  • Moi Hoon Yap
  • John Darby
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8815)


Recent works on human action recognition have focused on representing and classifying articulated body motion. These methods require a detailed knowledge of the action composition both in the spatial and temporal domains, which is a difficult task, most notably under real-time conditions. As such, there has been a recent shift towards the exemplar paradigm as an efficient low-level and invariant modelling approach. Motivated by recent success, we believe a real-time solution to the problem of human action recognition can be achieved. In this work, we present an exemplar-based approach where only a single action sequence is used to model an action class. Notably, rotations for each pose are parameterised in Exponential Map form. Delegate exemplars are selected using k-means clustering, where the cluster criteria is selected automatically. For each cluster, a delegate is identified and denoted as the exemplar by means of a similarity function. The number of exemplars is adaptive based on the complexity of the action sequence. For recognition, Dynamic Time Warping and template matching is employed to compare the similarity between a streamed observation and the action model. Experimental results using motion capture demonstrate our approach is superior to current state-of-the-art, with the additional ability to handle large and varied action sequences.


Human action recognition Motion capture Exponential map Online recognition Template matching Dynamic time warping 


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  1. 1.
    Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing 28(6), 976–990 (2010)CrossRefGoogle Scholar
  2. 2.
    Elgammal, A., Shet, V., Yacoob, Y., Davis, L.S.: Learning dynamics for exemplar-based gesture recognition. In: CVPR, pp. 571–578. IEEE Computer Society, Washington, DC (2003)Google Scholar
  3. 3.
    Barnachon, M, Bouakaz, S., Boufama, B., Guillou, E.: Ongoing human action recognition with motion capture. Pattern Recognition (2013)Google Scholar
  4. 4.
    Grassia, F.S.: Practical parameterization of rotations using the exponential map. Journal of Graphics Tools 3(3), 29–48 (1998)CrossRefGoogle Scholar
  5. 5.
    Bregler, C., Malik, J.: Tracking people with twists and exponential maps. In: CVPR, pp. 8–15 (1998)Google Scholar
  6. 6.
    Taylor, G.W., Hinton, G.E., Roweis, S.: Modeling human motion using binary latent variables. In: NIPS, p. 2007 (2006)Google Scholar
  7. 7.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proc. of the Fifth Berkeley Symposium on Mathematrical Statistics and Probability, pp. 281–297. University of California Press (1967)Google Scholar
  8. 8.
    Ketchen, D., Shook, C.: The application of cluster analysis in strategic management research: An analysis and critique. Strategic Management Journal 17(6), 441–458 (1996)CrossRefGoogle Scholar
  9. 9.
    Carnegie Mellon University Motion Capture Dataset. The data used in this project was obtained from The database was created with funding from NSF EIA-0196217Google Scholar
  10. 10.
    Müller, M., Röder, T., Clausen, M., Eberhardt, B., Krüger, B., Weber, A.: Documentation mocap database HDM05. Universität Bonn, Tech. Rep. CG-2007-2 (2007)Google Scholar
  11. 11.
    Müller, M., Baak, A., Seidel, H.-P.: Efficient and robust annotation of motion capture data. In: ACM SIGGRAPH/Eurographics Symposium on Computer Animation, New Orleans, LA, pp. 17–26 (August 2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Daniel Leightley
    • 1
    Email author
  • Baihua Li
    • 1
  • Jamie S. McPhee
    • 2
  • Moi Hoon Yap
    • 1
  • John Darby
    • 1
  1. 1.School of Computing, Mathematics and Digital TechnologyManchester Metropolitan UniversityManchesterUK
  2. 2.School of Healthcare ScienceManchester Metropolitan UniversityManchesterUK

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