Human Action Recognition Based on Radon Transform

  • Yan Chen
  • Qiang Wu
  • Xiangjian He
Part of the Studies in Computational Intelligence book series (SCI, volume 346)


A new feature description is used for human action representation and recognition. Features are extracted from the Radon transforms of silhouette images. Using the features, key postures are selected. Key postures are combined to construct an action template for each action sequence. Linear Discriminant Analysis (LDA) is applied to obtain low dimensional feature vectors. Different classification methods are used for human action recognition. Experiments are carried out based on a publicly available human action database.


Linear Discriminant Analysis Independent Component Analysis Action Sequence Independent Component Analysis Action Video 
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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  1. 1.
  2. 2.
    Altman, E.I., Marco, G., Varetto, F.: Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). New York University Salomon Center, Leonard N. Stern School of Business (1993)Google Scholar
  3. 3.
    Baraldi, A., Blonda, P.: A survey of fuzzy clustering algorithms for pattern recognition. II. IEEE Transactions on Systems, Man, and Cybernetics, Part B 29(6), 786–801 (1999)CrossRefGoogle Scholar
  4. 4.
    Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2 (2005)Google Scholar
  5. 5.
    Boulgouris, N.V., Hatzinakos, D., Plataniotis, K.N.: Gait recognition: a challenging signal processing technology for biometric identification. IEEE Signal Processing Magazine 22(6), 78–90 (2005)CrossRefGoogle Scholar
  6. 6.
    Calic, J., Izuierdo, E.: Efficient key-frame extraction and video analysis. In: Proceedings of International Conference on Information Technology: Coding and Computing, pp. 28–33 (2002)Google Scholar
  7. 7.
    Chen, D.Y., Liao, H.Y.M., Tyan, H.R., Lin, C.W.: Automatic Key Posture Selection for Human Behavior Analysis (2005)Google Scholar
  8. 8.
    Cooke, T.: Two Variations on Fisher’s Linear Discriminant for Pattern Recognition. IEEE Transactions On Pattern Analysis And Machine Intelligence, 268–273 (2002)Google Scholar
  9. 9.
    Deans, S.R.: The Radon Transform and Some of Its Applications. A Wiley-Interscience Publication, New York (1983)zbMATHGoogle Scholar
  10. 10.
    Efros, A.A., Berg, A.C., Mori, G., Malik, J.: Recognizing action at a distance. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 726–733 (2003)Google Scholar
  11. 11.
    Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972 (2007)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Fukunaga, K.: Introduction to statistical pattern recognition. Academic Press, London (1990)zbMATHGoogle Scholar
  13. 13.
    Hyvärinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural networks 13(4-5), 411–430 (2000)CrossRefGoogle Scholar
  14. 14.
    İkizler, N., Duygulu, P.: Human action recognition using distribution of oriented rectangular patches. In: Elgammal, A., Rosenhahn, B., Klette, R. (eds.) Human Motion 2007. LNCS, vol. 4814, pp. 271–284. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Jolliffe, I.T.: Principal component analysis. Springer, New York (2002)zbMATHGoogle Scholar
  16. 16.
    Lim, I.S., Thalmann, D.: Swiss Federal Inst Of Technology Lausanne (Switzerland). Key-posture extraction out of human motion data by curve simplification (2001)Google Scholar
  17. 17.
    Lv, F., Nevatia, R.: Single view human action recognition using key pose matching and viterbi path searching. In: IEEE CVPR, pp. 1–8 (2007)Google Scholar
  18. 18.
    Martinez, A.M., Kak, A.C.: Pca versus lda. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 228–233 (2001)CrossRefGoogle Scholar
  19. 19.
    Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Mullers, K.R.: Fisher discriminant analysis with kernels. In: Proceedings of the 1999 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing IX, pp. 41–48 (1999)Google Scholar
  20. 20.
    Pavlovic, V., Garg, A., Kasif, S.: A Bayesian framework for combining gene predictions*, pp. 19–27 (2002)Google Scholar
  21. 21.
    Platt, J.: Sequential minimal optimization: A fast algorithm for training support vector machines. Advances in Kernel Methods-Support Vector Learning, 208 (1999)Google Scholar
  22. 22.
    Quinlan, J.R.: Induction of decision trees. Machine learning 1(1), 81–106 (1986)Google Scholar
  23. 23.
    Radon, J.: Über die Bestimmung von Funktionen durch ihre Integralwerte längs gewisser Mannigfaltigkeiten. Berichte Sächsische Akademie der Wissenschaften, Leipzig, Mathematisch-Physikalische Klasse 69, 262–277 (1917)Google Scholar
  24. 24.
    Singh, M., Mandal, M., Basu, A.: Pose recognition using the Radon transform. In: 48th Midwest Symposium on Circuits and Systems, pp. 1091–1094 (2005)Google Scholar
  25. 25.
    Theodoridis, S., Koutroumbas, K.: Pattern recognition. Academic Press, London (2006)zbMATHGoogle Scholar
  26. 26.
    Thurau, C.: Behavior histograms for action recognition and human detection. In: Elgammal, A., Rosenhahn, B., Klette, R. (eds.) Human Motion 2007. LNCS, vol. 4814, pp. 299–312. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  27. 27.
    Tominaga, Y.: Comparative study of class data analysis with PCA-LDA, SIMCA, PLS, ANNs, and k-NN. Chemometrics and Intelligent Laboratory Systems 49(1), 105–115 (1999)CrossRefMathSciNetGoogle Scholar
  28. 28.
    Toyama, K., Blake, A.: Probabilistic tracking with exemplars in a metric space. International Journal of Computer Vision 48(1), 9–19 (2002)CrossRefzbMATHGoogle Scholar
  29. 29.
    Vapnik, V.: Estimation of dependences based on empirical data. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  30. 30.
    Wang, L., Suter, D.: Learning and matching of dynamic shape manifolds for human action recognition. IEEE Transactions on Image Processing 16(6), 1646–1661 (2007)CrossRefMathSciNetGoogle Scholar
  31. 31.
    Wang, Y., Huang, K., Tan, T.: Human activity recognition based on r transform. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (2007)Google Scholar
  32. 32.
    Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques with Java implementations. ACM SIGMOD Record 31(1), 76–77 (2002)CrossRefGoogle Scholar
  33. 33.
    Yeung, K.Y., Ruzzo, W.L.: Principal component analysis for clustering gene expression data, pp. 763–774 (2001)Google Scholar
  34. 34.
    Yu, H., Yang, J.: A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recognition 34(10), 2067–2070 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yan Chen
    • 1
  • Qiang Wu
    • 1
  • Xiangjian He
    • 1
  1. 1.Centre for Innovation in IT Services and Applications (iNext)University of TechnologySydneyAustralia

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