MULTIPLE CUE DATA FUSION USING MARKOV RANDOM FIELDS FOR MOTION DETECTION

Marc Vivet, Brais Martínez, Xavier Binefa

Abstract

We propose a new method for Motion Detection using stationary camera, where the information of different motion detectors which are not robust but light in terms of computation time (what we will call weak motion detector (WMD)) are merged with spatio-temporal Markov Random Field to improve the results. We put the strength, instead of on the weak motion detectors, on the fusion of their information. The main contribution is to show how the MRF can be modeled for obtaining a robust result. Experimental results show the improvement and good performance of the proposed method.

References

  1. Ahmed M. Elgammal, David Harwood, L. S. D. (2000). Non-parametric model for background subtraction. In Lecture Notes In Computer Science.
  2. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer, London, 1rst edition.
  3. Bohyung Han, Dorin Comaniciu, L. D. (2004). Sequential kernel density approximation through mode propagation: Applications to background modeling. In Asian Conference on Computer Vision (ACCV).
  4. Desa, S. M. and Salih, Q. A. (2004). Image subtraction for real time moving object extraction. In Proceedings of the International Conference on Computer Graphics, Imaging and Visualization (CGIV).
  5. Felzenszwalb, P. F. and Huttenlocher, D. P. (2004). Efficient belief propagation for early vision. In In CVPR.
  6. J.C.MacKay, D. (2003). Information Theory, Inference, and Learning Algorithms. Cambridge.
  7. K. Toyama, J. Krumm, B. B. and Meyers, B. (1999). Wallflower: Principles and practice of background maintenance. In International Conference on Computer Vision (ICCV).
  8. Kindermann, R. and Snell, J. L. (1980). Markov Random Fields and Their Applications. American Mathematical Society, 1rst edition.
  9. Kohli, P. and Torr, P. H. S. (2005). Effciently solving dynamic markov random fields using graph cuts. In International Conference on Computer Vision (ICCV).
  10. Koller, D., Weber, J., and Malik, J. (1994). Robust multiple car tracking with occlusion reasoning. In European Conference on Computer Vision (ECCV).
  11. Lo, B. and Velastin, S. (2000). Automatic congestion detection system for underground platforms. In Proc. of 2001 Int. Symp. on Intell. Multimedia, Video and Speech Processing.
  12. N.M. Oliver, B. R. and Pentland, A. (2000). A bayesian computer vision system for modeling human interactions. In Pattern Analysis and Machine Intelligence (PAMI).
  13. Qian, R. J. and Huang, T. S. (1997). Object detection using hierarchical mrf and map estimation. In CVPR 1997, Conference on Computer Vision and Pattern Recognition.
  14. R. Cucchiara, C. Grana, M. P. and Prati, A. (2003). Detecting moving objects, ghosts and shadows in video streams. In Pattern Analysis and Machine Intelligence (PAMI).
  15. Rittscher, J., Kato, J., Joga, S., and Blake, A. (2000). A probabilistic background model for tracking. In International Conference on Computer Vision (ICCV).
  16. Stauffer, C. and Grimson, W. E. L. (1999). Adaptive background mixture models for real-time tracking. In Computer Vision and Pattern Recognition (CVPR).
  17. Wang, D., Feng, T., yeung Shum, H., and Ma, S. (2002). A novel probability model for background maintenance and subtraction. In In Int. Conf. on Vision Interface.
  18. Weiss, Y. and Freeman, W. T. (2001). Correctness of belief propagation in gaussian graphical models of arbitrary topology. In Neural Computation.
  19. Yedidia, J. S., Freeman, W. T., and Weiss, Y. (2005). Constructing free energy approximations and generalized belief propagation algorithms. In IEEE Transactions on Information Theory.
  20. Yin, Z. and Collins, R. T. (2007). Belief propagation in a 3d spatio-temporal mrf for moving object detection. In CVPR 2007, Conference on Computer Vision and Pattern Recognition.
Download


Paper Citation


in Harvard Style

Vivet M., Martínez B. and Binefa X. (2009). MULTIPLE CUE DATA FUSION USING MARKOV RANDOM FIELDS FOR MOTION DETECTION . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 529-534. DOI: 10.5220/0001802605290534


in Bibtex Style

@conference{visapp09,
author={Marc Vivet and Brais Martínez and Xavier Binefa},
title={MULTIPLE CUE DATA FUSION USING MARKOV RANDOM FIELDS FOR MOTION DETECTION},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={529-534},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001802605290534},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)
TI - MULTIPLE CUE DATA FUSION USING MARKOV RANDOM FIELDS FOR MOTION DETECTION
SN - 978-989-8111-69-2
AU - Vivet M.
AU - Martínez B.
AU - Binefa X.
PY - 2009
SP - 529
EP - 534
DO - 10.5220/0001802605290534