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Occlusion Management in Sequential Mean Field Monte Carlo Methods

  • Carlos Medrano
  • Raúl Igual
  • Carlos Orrite
  • Inmaculada Plaza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6669)

Abstract

In this paper we analyse the problem of occlusions under a Mean Field Monte Carlo approach. This kind of approach is suitable to approximate inference in problems such as multitarget tracking, in which this paper is focused. It leads to a set of fixed point equations, one for each target, that can be solved iteratively. While previous works considered independent likelihoods and pairwise interactions between objects, in this work we assume a more realistic joint likelihood that helps to cope with occlusions. Since the joint likelihood can truly depend on several objects, a high dimensional integral appears in the raw approach. We consider an approximation to make it computationally feasible. We have tested the proposed approach on football and indoor surveillance sequences, showing that a low number of failures can be achieved.

Keywords

Multitarget tracking occlusions mean field 

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References

  1. 1.
    Isard, M., MacCormick, J.: BraMBLe: A Bayesian Multiple-Blob Tracker. In: Proceedings of the IEEE ICCV, vol. 2, pp. 34–41 (2001)Google Scholar
  2. 2.
    Lanz, O.: Approximate Bayesian Multibody Tracking. IEEE Transactions on PAMI 9(28), 1436–1449 (2006)CrossRefGoogle Scholar
  3. 3.
    Zhao, T., Nevatia, R., Wu, B.: Segmentation and tracking of multiple humans in crowded environments. IEEE Transactions on PAMI 30(7) (2008)Google Scholar
  4. 4.
    Hua, G., Wu, Y.: Sequential mean field variational analysis of structured deformable shapes. Computer Vision and Image Understanding 101, 87–99 (2006)CrossRefGoogle Scholar
  5. 5.
    Hua, G., Wu, Y.: A decentralized probabilistic approach to articulated body tracking. Computer Vision and Image Understanding 108, 272–283 (2007)CrossRefGoogle Scholar
  6. 6.
    Medrano, C., Herrero, J.E., Martinez, J., Orrite, C.: Mean field approach for tracking similar objects. Computer Vision and Image Understanding 113, 907–920 (2009)CrossRefGoogle Scholar
  7. 7.
    Yao, J., Odobez, J.M.: Multi-Camera Multi-Person 3D Space Tracking with MCMC in Surveillance Scenarios. In: ECCV Workshop on Multi Camera and Multi-modal Sensor Fusion Algorithms and Applications ECCV-M2SFA2 (2008)Google Scholar
  8. 8.
    Joo, S., Chellappa, R.: A Multiple-Hypothesis Approach for Multiple Visual Tracking. IEEE Transactions on Image Processing 11(16), 2849–2854 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Carlos Medrano
    • 1
  • Raúl Igual
    • 2
  • Carlos Orrite
    • 1
  • Inmaculada Plaza
    • 2
  1. 1.CVLabAragon Institute for Engineering ResearchZaragozaSpain
  2. 2.EduQTechE.U. PolitécnicaTeruelSpain

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