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Action Recognition Based on the Feature Trajectories

  • Ji-Xiang Du
  • Kai Yang
  • Chuan-Min Zhai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7390)

Abstract

In this paper, we proposed an intuitive approach on videos based on the feature trajectories. In contrast to the spatio-temporal interest points, feature trajectories is a more adapted representation and able to benefit from the rich motion information which is described by the HOF. The main contribution of our paper is the combination of the HOG and HOF feature description which represented the shape information and the motion information. We present recognition results on a variety of dataset such as YouTobe and KTH, compared to previous work, the results showed that our algorithm is more efficient and accurate compared with the previous work.

Keywords

action recognition trajectory features spatio-temporal features HOG HOF 

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References

  1. 1.
    Gavrila, D.M.: The Visual Analysis of Human Movement: A Survey. CVIU 73, 82–98 (1999)zbMATHGoogle Scholar
  2. 2.
    Blank, M., Gorelick, L., Shechtman, E., et al.: Actions as Space-time Shapes. In: ICCV (2005)Google Scholar
  3. 3.
    Moeslund, T., Hilton, A., Kruger, V.: A Survey of Advances in Vision-based Human Motion Capture and Analysis. CVIU 104(2-3), 90–126 (2006)Google Scholar
  4. 4.
    Johanssonm, G.: Visual Perception of Biological Motion and A Model for Its Analysis. Perception and Psychophysics 14, 201–211 (1973)CrossRefGoogle Scholar
  5. 5.
    Mesing, R., Pal, C., Kautz, H.: Activity Recognition Using the Velocity Histories of Tracked Keypoints. In: ICCV (2009)Google Scholar
  6. 6.
    Sun, J., Wu, X., Ya, S., et al.: Hierarchical Spatio-temporal Context Modeling for Action Recognition. In: CVPR (2009)Google Scholar
  7. 7.
    Matikainen, P., Hebert, M., Sukthankar, R.: Trajectory: Action Recognition Through the Motion Analysis of Tracked Features. In: ICCV Workshop on Video-oriented Object and Event Classification (2009)Google Scholar
  8. 8.
    Bouguet, J.: Pyramidal Implementation of The Lucas-Kanade Feature Tracker Description of The Algorithm. Technical report, Intel Corporation, Microprocessor Research Labs (1999)Google Scholar
  9. 9.
    Lucas, D., Kanade, T.: An Iterative Image Registration Technique with An Application to Stereo Vision. In: IJCAI (1981)Google Scholar
  10. 10.
  11. 11.
    Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning Realistic Human Actions from Movies. In: CVPR (2008)Google Scholar
  12. 12.
    Schindler, K., Gool, L.: Action Snippets: How Many Frames Does Human Action Recognition Require? In: CVPR (2008)Google Scholar
  13. 13.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR (2005)Google Scholar
  14. 14.
    Dalal, N., Triggs, B., Schmid, C.: Human Detection Using Oriented Histograms of Flow and Appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ji-Xiang Du
    • 1
  • Kai Yang
    • 1
  • Chuan-Min Zhai
    • 1
  1. 1.Department of Computer Science and TechnologyHuaqiao UniversityXiamenChina

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