AN ARTICULATED MODEL WITH A KALMAN FILTER FOR REAL TIME VISUAL TRACKING - Application to the Tracking of Pedestrians with a Monocular Camera

Youssef Rouchdy

Abstract

This work presents a method for the visual tracking of articulated targets in image sequences in real time. Each part of the target object is considered as a region of interest and tracked by a parametric transformation. Prior geometric and dynamic informations about the target are introduced with a Kalman filter to guide the evolution of the tracking process of regions. An articulated model with two areas is proposed and applied to track pedestrians in the urban image sequences.

References

  1. Arnaud, E., Memin, E., and Cernuschi-Frias, B. (2004).
  2. Benhimane, S. and Malis, E. (2004).
  3. Real-time imagebased tracking of planes using efficient second-order minimization. In In IEEE/RSJ International Conference on Intelligent Robots Systems, Sendai, Japan, October 2004.
  4. , Black, M. J. and Fleet, D. J. (1999). Probabilistic detection and tracking of motion discontinuities. In ICCV (1), pages 551-558.
  5. Blake, A., North, B., and Isard, M. (1999). Learning multiclass dynamics. Advances in Neural Information Processing Systems, 11:389-395.
  6. Bregler, C., Malik, J., and Pullen, K. (2004). Twist based acquisition and tracking of animal and human kinematics. Int. J. Comput. Vision, 56(3):179-194.
  7. Cuzol, A., Hellier, P., and Mmin, E. (2007). A low dimensional fluid motion estimator. Int. Journ. on Computer Vision.
  8. Del Moral, P. (1997). Nonlinear filtering: interacting particle resolution. C. R. Acad. Sci. Paris Sér. I Math., 325(6):653-658.
  9. Doucet, A., de Freitas, N., and Gordon, N., editors (2002). Sequential Monte Carlo Methods in Practice. Statistics for Engineering and Information Science. Springer-Verlag, New York Berlin Heidelberg.
  10. Faugeras, O., Luong, Q.-T., and Papadopoulou, T. (2001). The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications. MIT Press, Cambridge, MA, USA.
  11. Gavrila, D. M. (1999). The visual analysis of human movement: A survey. Computer Vision and Image Understanding: CVIU, 73(1):82-98.
  12. Gavrila, D. M. and Davis, L. S. (1995). Towards 3d modelbased tracking and recognition of human movement. In Proc. of the IEEE International Workshop on Face and Gesture Recognition, pages 272-277, Zurich, Switzerland.
  13. Gordon, N. (1993). Bayesian Methods for Tracking. PhD thesis, University of London.
  14. Kakadiaris, I. A. and Metaxas, D. (1996). Model-based estimation of 3D human motion with occlusion based on active multi-viewpoint selection. In Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR 7896), page 81, Washington, DC, USA.
  15. Kalman, R. E. and Bucy, R. S. (1961). New results in linear filtering and prediction theory. Trans. ASME Ser. D. J. Basic Engrg., 83:95-108.
  16. Malis, E. (April 2004). Improving vision-based control using efficient secondorder minimization techniques. In ICRA'04, New Orleans.
  17. Morency, L., Rahimi, A., and Darrell, T. (2003). Adaptive view-based appearance models. In Proc. IEEE Conf. on Comp. Vision and Pattern Recogn., pages 803-810.
  18. Murray, R. M., Sastry, S. S., and Zexiang, L. (1994). A Mathematical Introduction to Robotic Manipulation. CRC Press, Inc., Boca Raton, FL, USA.
  19. Perez, P., Hue, C., Vermaak, J., and Gangnet, M. (2002). Color-based probabilistic tracking. In ECCV, number 2350 in LNCS, pages 661-675.
  20. Rouchdy, Y., Pousin, J., Schaerer, J., and Clarysse, P. (2007). A nonlinear elastic deformable template for soft structure segmentation. Application to the heart segmentation in MRI. Inverse Problems, 23:1017- 1035.
  21. Sidenbladh, H. and Black, M. (2003). Learning the statistics of people in images and video. Int. Journ. on Computer Vision, 54(1-3):183-209.
  22. Sidenbladh, H. and Black, M. J. (2002). Learning the statistics of people in images and video. Int. Journal of Computer Vision, 54.
  23. Weiss, Y. and Adelson, E. H. (1996). A unified mixture framework for motion segmentation: Incorporating spatial coherence and estimating the number of models. In Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR 7896), page 321, Washington, DC, USA.
  24. Wu, Y. and Huang, T. (2001). A co-inference approach to robust visual tracking. In Proc. IEEE Conf. on Comp. Vision, pages 26-33.
  25. Zhang, X., Liu, Y., and Huang, T. S. (2006). Motion analysis of articulated objects from monocular images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4):625-636.
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Paper Citation


in Harvard Style

Rouchdy Y. (2008). AN ARTICULATED MODEL WITH A KALMAN FILTER FOR REAL TIME VISUAL TRACKING - Application to the Tracking of Pedestrians with a Monocular Camera . In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 686-693. DOI: 10.5220/0001089706860693


in Bibtex Style

@conference{visapp08,
author={Youssef Rouchdy},
title={AN ARTICULATED MODEL WITH A KALMAN FILTER FOR REAL TIME VISUAL TRACKING - Application to the Tracking of Pedestrians with a Monocular Camera},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={686-693},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001089706860693},
isbn={978-989-8111-21-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)
TI - AN ARTICULATED MODEL WITH A KALMAN FILTER FOR REAL TIME VISUAL TRACKING - Application to the Tracking of Pedestrians with a Monocular Camera
SN - 978-989-8111-21-0
AU - Rouchdy Y.
PY - 2008
SP - 686
EP - 693
DO - 10.5220/0001089706860693