A Framework for Suspicious Action Detection with Mixture Distributions of Action Primitives

  • Yoshio Iwai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)

Abstract

In this paper, we propose a generic framework for detecting suspicious actions with mixture distributions of action primitives, of which collection represents human actions. The framework is based on Bayesian approach and the calculation is performed by Sequential Monte Carlo method, also known as Particle filter. Sequential Monte Carlo is used to approximate the distributions for fast calculation, but it tends to converge one local minimum. We solve that problem by using mixture distributions of action primitives. By this approach, the system can recognize people’s actions as whether suspicious actions or not.

References

  1. 1.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), vol. 1, pp. 511–518 (2001)Google Scholar
  2. 2.
    Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Oliver, N.M., Rosario, B., Pentland, A.P.: A Bayesian computer vision system for modeling human interactions. IEEE Trans. on PAMI 22(8), 831–843 (2000)CrossRefGoogle Scholar
  4. 4.
    Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. IJCV 29, 5–28 (1998)CrossRefGoogle Scholar
  5. 5.
    Sidenbladh, H., Black, M.J., Sigal, L.: Implicit probabilistic models of human motion for synthesis and tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 784–800. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Matsumura, A., Iwai, Y., Yachida, M.: Stochastic action recognition from omnidirectional images. In: Proc. of Asian Conf. on Computer Vision, vol. 1, pp. 120–125 (2004)Google Scholar
  7. 7.
    Yamazawa, K., Yagi, Y., Yachida, M.: Omnidirectional imaging with hyperboloidal projection. In: Proc. of the Int. Conf. on Intelligent Robots and Systems(IROS 1993), vol. 2, pp. 1029–1034 (1993)Google Scholar
  8. 8.
    Mituyoshi, T., Yagi, Y., Yachida, M.: Real-time human feature acquisition and human tracking by omnidirectional image sensor. In: Proc. IEEE Conf. on Multisensor Fusion and Integration for Intelligent Systems, pp. 258–263 (2003)Google Scholar
  9. 9.
    Black, M.J., Jepson, A.D.: A probabilistic framework for matching temporal trajectories: CONDENSATION-based recognition of gestures and expressions. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 909–924. Springer, Heidelberg (1998)Google Scholar
  10. 10.
    Himeno, M., Himeno, R.: The effect of crossover and mutation to DC in early generations for multimodal function optimization. IEICE (D-I) J85-D-I(11), 1015–1027 (2002)Google Scholar
  11. 11.
    Vermaak, J., Doucet, A., Pérez, P.: Maintaining multi-modality through mixture tracking. In: Proc. 9th ICCV, vol. 2, pp. 1110–1116 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yoshio Iwai
    • 1
  1. 1.Graduate School of Engineering ScienceOsaka UniversityOsakaJapan

Personalised recommendations