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Learning and Regularizing Motion Models for Enhancing Particle Filter-Based Target Tracking

  • Francisco Madrigal
  • Mariano Rivera
  • Jean-Bernard Hayet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7088)

Abstract

This paper describes an original strategy for using a data-driven probabilistic motion model into particle filter-based target tracking on video streams. Such a model is based on the local motion observed by the camera during a learning phase. Given that the initial, empirical distribution may be incomplete and noisy, we regularize it in a second phase. The hybrid discrete-continuous probabilistic motion model learned this way is then used as a sampling distribution in a particle filter framework for target tracking. We present promising results for this approach in some common datasets used as benchmarks for visual surveillance tracking algorithms.

Keywords

Video Sequence Motion Model Target Tracking Observation Model Visual Tracking 
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.

References

  1. 1.
    Leibe, B., Schindler, K., Van Gool, L.: Coupled detection and trajectory estimation for multi-object tracking. In: Proc. Int. Conf. on Computer Vision (2007)Google Scholar
  2. 2.
    Kuo, C.H., Huang, C., Nevatia, R.: Multi-target tracking by on-line learned discriminative appearance models. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2010), pp. 685–692 (2010)Google Scholar
  3. 3.
    Fragkiadaki, K., Shi, J.: Detection free tracking: Exploiting motion and topology for tracking and segmenting under entanglement. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, CVPR 2011 (2011)Google Scholar
  4. 4.
    Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2005), pp. 878–885 (2005)Google Scholar
  5. 5.
    Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2000), vol. 2, pp. 142–149 (2000)Google Scholar
  6. 6.
    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, Part I. LNCS, vol. 2350, pp. 784–800. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Bouguet, J.Y.: Pyramidal implementation of the Lucas Kanade feature tracker description of the algorithm. In: USENIX Technical Conference (1999)Google Scholar
  8. 8.
    Shi, J., Tomasi, C.: Good features to track. In: Int. Conf. on Computer Vision and Pattern Recognition (CVPR 1994), pp. 593–600 (1994)Google Scholar
  9. 9.
    Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, New York (2006)zbMATHGoogle Scholar
  10. 10.
    Ellis, A., Ferryman, J.: Pets2010 and pets2009 evaluation of results using individual ground truth single views. In: Proc. of the IEEE Int. Conf. on Advanced Video and Signal Based Surveillance (AVSS), pp. 135–142 (2010)Google Scholar
  11. 11.
    Madrigal, F., Hayet, J.: Multiple view, multiple target tracking with principal axis-based data association. In: Proc. of the IEEE Int. Conf. on Advanced Video and Signal based Surveillance, AVSS (2011)Google Scholar
  12. 12.
    Doucet, A., De Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo methods in practice. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  13. 13.
    Perez, P., Vermaak, J., Blake, A.: Data fusion for visual tracking with particles. Proc. of the IEEE 92(3), 495–513 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Francisco Madrigal
    • 1
  • Mariano Rivera
    • 1
  • Jean-Bernard Hayet
    • 1
  1. 1.Centro de Investigación en MatemáticasGuanajuatoMéxico

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