Skip to main content
Log in

Human action detection via boosted local motion histograms

  • Short Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

This paper presents a novel learning method for human action detection in video sequences. The detecting problem is not limited in controlled settings like stationary background or invariant illumination, but studied in real scenarios. Spatio-temporal volume analysis for actions is adopted to solve the problem. To develop effective representation while remaining resistant to background motions, only motion information is exploited to define suitable descriptors for action volumes. On the other hand, action models are learned by using boosting techniques to select discriminative features for efficient classification. This paper also shows how the proposed method enables learning efficient action detectors, and validates them on publicly available datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Dalal N., Triggs B.: Histograms of oriented gradients for human detection. Comput. Vis. Pattern Recognit. 2, 886–893 (2005)

    Google Scholar 

  2. Dalal N., Triggs B., Schmid C.: Human detection using oriented histograms of flow and appearance. Eur. Conf. Comput. Vis. 2, 428–441 (2006)

    Google Scholar 

  3. Dollár, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: VS-PETS, pp. 65–72 (2005)

  4. Friedman J., Hastie T., Tibshirani R.: Additive logistic regression: A statistical view of boosting. Ann. Stat. 38(2), 337–374 (2000)

    Article  MathSciNet  Google Scholar 

  5. Gavrila D.M.: The visual analysis of human movement: A survey. Comput. Vis. Image Underst. 73(1), 82–98 (1999)

    Article  MATH  Google Scholar 

  6. Gennert, M.A., Negahdaripour, S.: Relaxing the brightness constancy assumption in computing optical flow. A.I. Memo, p. 975. MIT Press, Cambridge (1987)

  7. Horn B.K., Schunck B.G.: Determining optical flow. Artif. Intell. 17, 185–203 (1981)

    Article  Google Scholar 

  8. Ke Y., Sukthankar R., Hebert M.: Efficient visual event detection using volumetric features. IEEE Int. Conf. Comput. Vis. 1, 166–173 (2005)

    Google Scholar 

  9. Kim, T.K., Wong, S.F., Cipolla, R.: Tensor canonical correlation analysis for action classification. In: CVPR (2007)

  10. Laptev, I.: Improvements of object detection using boosted histograms. In: BMVC, vol. 3, pp. 949–958 (2006)

  11. Laptev I., Lindeberg T.: Space-time interest points. IEEE Int. Conf. Comput. Vis. 1, 432–439 (2003)

    Article  Google Scholar 

  12. Laptev, I., Pérez, P.: Retrieving actions in movies. IEEE Int. Conf. Comput. Vis., pp. 432–439 (2007)

  13. Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. MRL technical report (2002)

  14. Niebles, J.C., Wang, H., Li, F.F.: Unsupervised learning of human action categories using spatial-temporal words. In: BMVC (2006)

  15. Porikli F.: Integral histogram: A fast way to extract histograms in cartesian spaces. Comput. Vis. Pattern Recognit. 1, 829–836 (2005)

    Google Scholar 

  16. Proesmans, M., Gool, L.J.V., Pauwels, E.J., Oosterlinck, A.: Determination of optical flow and its discontinuities using non-linear diffusion. In: European Conference on Computer Vision, pp. 295–304. Springer, London (1994)

  17. Ramanan, D., Forsyth, D.A.: Automatic annotation of everyday movements. In: Advances in Neural Information Processing Systems, vol. 16. MIT Press, Cambridge (2004)

  18. Schuldt C., Laptev I., Caputo B.: Recognizing human actions: A local svm approach. Int. Conf. Pattern Recognit. 3, 32–36 (2004)

    Google Scholar 

  19. Shah M., Jain R.: Motion-Based Recognition. Computational Imaging and Vision Series. Kluwer, Dordrecht (1997)

    Google Scholar 

  20. Viola P., Jones M.: Rapid object detection using a boosted cascade of simple features. Comput. Vis. Pattern Recognit. 1, 511–518 (2001)

    Google Scholar 

  21. Wong, S.F., Kim, T.K., Cipolla, R.: Learning motion categories using both semantic and structural information. In: CVPR (2007)

  22. Yilmaz A., Shah M.: Recognizing human actions in videos acquired by uncalibrated moving cameras. IEEE Int. Conf. Comput. Vis. 1, 150–157 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qingshan Luo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Luo, Q., Kong, X., Zeng, G. et al. Human action detection via boosted local motion histograms. Machine Vision and Applications 21, 377–389 (2010). https://doi.org/10.1007/s00138-008-0168-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-008-0168-5

Keywords

Navigation