Advertisement

Human Action Recognition Using Non-separable Oriented 3D Dual-Tree Complex Wavelets

  • Rashid Minhas
  • Aryaz Baradarani
  • Sepideh Seifzadeh
  • Q. M. Jonathan Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5996)

Abstract

This paper introduces an efficient technique for simultaneous processing of video frames to extract spatio-temporal features for fine activity detection and localization. Such features, obtained through motion-selectivity attribute of 3D dual-tree complex wavelet transform (3D-DTCWT), are used to train a classifier for categorization of an incoming video. The proposed learning model offers three core advantages: 1) significantly faster training stage than traditional supervised approaches, 2) volumetric processing of video data due to the use of 3D transform, 3) rich representation of human actions in view of directionality and shift-invariance of DTCWT. No assumptions of scene background, location, objects of interest, or point of view information are made for activity learning whereas bidirectional 2D-PCA is employed to preserve structure and correlation amongst neighborhood pixels of a video frame. Experimental results compare favorably to recently published results in literature.

Keywords

Feature Vector Discrete Wavelet Transform Extreme Learning Machine Video Frame Complex Wavelet 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ali, S., Basharat, A., Shah, M.: Chaotic invariants for human action recognition. In: Proc. of Int. Conf. on CV (2007)Google Scholar
  2. 2.
    Baradarani, A., Wu, J.: Moving object segmentation using the 9/7–10/8 dual-tree complex filter bank. In: Proc. of the 19th IEEE Int. Conf. on PR, Florida, pp. 7–11 (2008)Google Scholar
  3. 3.
    Black, M.J.: Explaining optical flow events with parameterized spatio-temporal models. In: Proc. of Int. Conf. on CVPR, pp. 1326–1332 (1999)Google Scholar
  4. 4.
    Brand, M., Oliver, N., Pentland, A.: Coupled HMM for Complex Action Recognition. In: Proc. of Int. Conf. on CVPR (1997)Google Scholar
  5. 5.
    Burns, T.J.: A non-homogeneous wavelet multiresolution analysis and its application to the analysis of motion, PhD thesis, Air Force Institute of Tech. (1993)Google Scholar
  6. 6.
    Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space time shapes. IEEE Trans. on PAMI, 2247–2253 (2007)Google Scholar
  7. 7.
    Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing (2005)Google Scholar
  8. 8.
    Jiang, H., Drew, M.S., Li, Z.N.: Successive convex matching for action detection. In: Proc. of Int. Conf. on CVPR (2006)Google Scholar
  9. 9.
    Kingsbury, N.G.: Complex wavelets for shift invariant analysis and filtering of signals. Journal of Applied and Computational Harmonic Analysis 10(3), 234–253 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Li, L.-J., Li, F.-F.: What, where and who? Classifying events by scene and object recognition. In: Proc. of Int. Conf. on CV (2007)Google Scholar
  11. 11.
    Liu, J., Ali, S., Shah, M.: Recognizing human actions using multiple features. In: Proc. of Int. Conf. on CVPR (2008)Google Scholar
  12. 12.
    Mori, G., Ren, X., Efros, A.A., Malik, J.: Recovering human body configurations: combining segmentation and recognition. In: Proc. of Int. Conf. on CVPR (2004)Google Scholar
  13. 13.
    Niebels, J. L.F.-F.: A hierarchical model of shape and appearance for human action classification. In: Proc. of Int. Conf. on CVPR (2007)Google Scholar
  14. 14.
    Selesnick, I.W.: Hilbert transform pairs of wavelet bases. IEEE Signal Processing Letters 8, 170–173 (2001)CrossRefGoogle Scholar
  15. 15.
    Selesnick, I.W., Li, K.Y.: Video denoising using 2D and 3D dual-tree complex wavelet transforms, Wavelet Applications in Signal and Image. In: Proc. SPIE 5207, San Diego (August 2003)Google Scholar
  16. 16.
    Selesnick, I.W., Shi, F.: Video denoising using oriented complex wavelet transforms. In: Proc. of the IEEE Int. Conf. on Acoust., Speech, and Signal Proc., May 2004, vol. 2, pp. 949–952 (2004)Google Scholar
  17. 17.
    Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.G.: The dual-tree complex wavelet transform – a coherent framework for multiscale signal and image processing. IEEE Signal Processing Magazine 6, 123–151 (2005)CrossRefGoogle Scholar
  18. 18.
    Smith, P., Victoria, N.D., Shah, M.: TemporalBoost for event recognition. In: Proc. of Int. Conf. on CV (2005)Google Scholar
  19. 19.
    Strang, G., Nguyen, T.: Wavelets and Filter Banks. Wellesley, Cambridge (1996)Google Scholar
  20. 20.
    Yang, J., Zhang, D., Frangi, F., Yang, J.-Y.: Two-dimensional PCA: a new approach to appearance based face representation and recognition. IEEE Trans. on PAMI (1), 131–137 (2004)Google Scholar
  21. 21.
    Yilmaz, A., Shah, M.: Actions sketch: a novel action representation. In: Proc. of Int. Conf. on CVPR (2005)Google Scholar
  22. 22.
    Yu, R., Baradarani, A.: Sampled-data design of FIR dual filter banks for dual-tree complex wavelet transforms. IEEE Trans. on Signal Proc. 56(7), 3369–3375 (2008)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rashid Minhas
    • 1
  • Aryaz Baradarani
    • 1
  • Sepideh Seifzadeh
    • 2
  • Q. M. Jonathan Wu
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of WindsorCanada
  2. 2.School of Computer ScienceUniversity of WindsorCanada

Personalised recommendations