Efficient Silhouette-based Input Methods for Reliable Human Action Recognition from Videos

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 325)

Abstract

In this paper, we deal the problem of classification of activities in videos by learning in a different way of modifying the usual input features to enrich the efficiency by reducing the space and time consumption. For efficient action recognition, representing important information is important for reliability. The motivation here is to extract binary silhouette (shape) information from real or raw videos. Most of the researchers are using sequence matching scheme consists of a set of successive silhouette frame that consumes much time and space for learning the system. Here, we adopted recently proposed subspace learning method: spectral regression discriminant analysis (SRDA) for dimensionality reduction. Median Hausdorff distance was used for similarity measures to match the embedded action trajectories. Then, action classification is achieved in a nearest neighbor framework. For efficient learning using SRDA, we use (i) raw silhouette, (ii) images obtained after doing distance transform (DT) in the raw silhouette, (iii) images obtained after extracting edges from the raw silhouettes—edge representation (iv) silhouette history image (SHI), and (v) silhouette energy image (SEI) that gives shape and motion information as an input feature. Using these different input methods, we achieved 100 % correct recognition rate (CRR) for each cases. From the result, it is evident that SHI and SEI is found effective input method than other methods in terms of time and space consumption.

Keywords

Action recognition Silhouette energy image Silhouette history image Spectral regression discriminant analysis (SRDA) Edge representation Distance transform 

References

  1. 1.
    M. Turk, A. Pentland, Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)CrossRefGoogle Scholar
  2. 2.
    R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification (2000)Google Scholar
  3. 3.
    X. He, S. Yan, Y. Hu, P. Niyogi, H. Zhang, Face recognition using laplacian faces. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 328–340 (2005)CrossRefGoogle Scholar
  4. 4.
    J.B. Tenenbaum, V. de Silva, J.C. Langford, A global geometric framework for non linear dimensionality reduction. Science 290, 2319–2323 (2000)CrossRefGoogle Scholar
  5. 5.
    S.T. Roweis, L.K. Saul, Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)CrossRefGoogle Scholar
  6. 6.
    M. Belkin, P. Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15, 1373–1396 (2003)CrossRefMATHGoogle Scholar
  7. 7.
    R.R. Coifman, S. Lafon, A.B. Lee, M. Maggioni, B. Nadler, F. Warner, S. Zucker, Geometric diffusions as a tool for harmonics analysis and structure definition of data. Proc. Natl. Acad. Sci. 102(21), 7426–7431 (2005)CrossRefGoogle Scholar
  8. 8.
    B. Schlkopf, A.J. Smola, Learning with Kernels (2002)Google Scholar
  9. 9.
    L. Wang, D. Suter, Recognizing human activities from silhouettes: motion subspace and factorial discriminative graphical model, in Computer Vision and Pattern Recognition (2007), pp. 1–8Google Scholar
  10. 10.
    S. Mika, G. Ratsch, J. Weston, B. Scholkopf, K.-R. Muller, Fisher discriminant analysis with kernels, in Proceedings of IEEE Neural Networks for Signal Processing Workshop (NNSP) (1999)Google Scholar
  11. 11.
    G. Baudat, F. Anouar, Generalized discriminant analysis using a kernel approach. Neural Comput. 12, 2385–2404 (2000)CrossRefGoogle Scholar
  12. 12.
    D. Cai, X. He, J. Han, Regularized locality preserving indexing via spectral regression, in ACM International Conference on Information and Knowledge Management (2007)Google Scholar
  13. 13.
    D. Cai, X. He, J. Han, Spectral regression for efficient regularized subspace learning, in IEEE International Conference on Computer Vision (ICCV) (2007)Google Scholar
  14. 14.
    D. Cai, X. He, J. Han, SRDA: an efficient algorithm for large scale discriminant analysis. IEEE Trans. Knowl. Data Eng. 20(1), 1–12 (2008)CrossRefGoogle Scholar
  15. 15.
    H. Scharr, Optimal second order derivative filter families for transparent motion estimation, in 15th European Signal Processing Conference (EUSIPCO 2007) (2007)Google Scholar
  16. 16.
    M. Ahmad, I. Parvin, S.-W. Lee, Silhouette history and energy image information for human movement recognition. J. Multimedia 5(1), 12–21 (2010)Google Scholar
  17. 17.
    L. Wang, D. Suter, Learning and matching of dynamic shape manifolds for human action recognition. IEEE Trans. Image Process. 16(6), 1646–1661 (2007)CrossRefMathSciNetGoogle Scholar
  18. 18.
    M. Blank, L. Gorelick, E. Shechtman, M. Irani, R. Basri, Actions as space-time shapes. IEEE Int. Conf. Comput. Vision 2, 1395–1402 (2005)Google Scholar
  19. 19.
    M. Blank, L. Gorelick, E. Shechtman, M. Irani, R. Basri, Actions as space-time shapes. IEEE Int. Conf. Comput. Vision 2, 1395–1402 (2005)Google Scholar
  20. 20.
    V. Kellokumpu, G. Zhao, M. Pietikainen, Texture based description of movements for activity analysis, in Proceedings of 3rd International Conference on Computer Vision Theory and Applications (2008)Google Scholar
  21. 21.
    K. Jia, D. Yeung, Human action recognition using Local Spatio-Temporal Discriminant Embedding, in IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  22. 22.
    F. Zheng, L. Shao, Z. Song, Eigen-space Learning Using Semi-supervised Diffusion Maps for Human Action Recognition, in ACM International Conference on Image and Video Retrieval (CIVR) (2010)Google Scholar
  23. 23.
    L. Shao, X. Chen, Histogram of body poses and spectral regression discriminant analysis for human action categorization, in Proceedings of the British Machine Vision Conference (2010)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.MS UniversityTirunelveliIndia
  2. 2.J.P. College of EngineeringAykudi, TenkasiIndia

Personalised recommendations