ARTICULATED HUMAN MOTION TRACKING WITH HPSO

Vijay John, Spela Ivekovic, Emanuele Trucco

Abstract

In this paper, we address full-body articulated human motion tracking from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional nonlinear optimisation and solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm which has gained popularity in recent years due to its ability to solve difficult nonlinear optimisation problems. Our tracking approach is designed to address the limits of particle filtering approaches: it initialises automatically, removes the need for a sequence-specific motion model and recovers from temporary tracking divergence through the use of a powerful hierarchical search algorithm (HPSO). We quantitatively compare the performance of HPSO with that of the particle filter (PF) and annealed particle filter (APF). Our test results, obtained using the framework proposed by (Balan et al., 2005) to compare articulated body tracking algorithms, show that HPSO’s pose estimation accuracy and consistency is better than PF and compares favourably with the APF, outperforming it in sequences with sudden and fast motion.

References

  1. Balan, A. O., Sigal, L., and Black, M. J. (2005). A quantitative evaluation of video-based 3d person tracking. In ICCCN 7805: Proceedings of the 14th International Conference on Computer Communications and Networks, pages 349-356. IEEE Computer Society.
  2. Caillette, F., Galata, A., and Howard, T. (2008). Real-time 3-d human body tracking using learnt models of behaviour. Computer Vision and Image Understanding, 109(2):112-125.
  3. Deutscher, J. and Reid, I. (2005). Articulated body motion capture by stochastic search. International Journal of Computer Vision, 61(2):185-205.
  4. Husz, Z., Wallace, A., and Green, P. (2007). Evaluation of a hierarchical partitioned particle filter with action primitives. In CVPR 2nd Workshop on Evaluation of Articulated Human Motion and Pose Estimation.
  5. Isard, M. and Blake, A. (1998). Condensation - conditional density propagation for visual tracking. International Journal of Computer Vision, 29(1):5-28.
  6. Ivekovic, S. and Trucco, E. (2006). Human body pose estimation with pso. In Proceedings of IEEE Congress on Evolutionary Computation (CEC 7806), pages 1256- 1263.
  7. Ivekovic, S., Trucco, E., and Petillot, Y. (2008). Human body pose estimation with particle swarm optimisation. Evolutionary Computation, 16(4).
  8. Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks, volume 4, pages 1942-1948.
  9. MacCormick, J. and Isard, M. (2000). Partitioned sampling, articulated objects, and interface-quality hand tracking. In Proceedings of the European Conference on Computer Vision (ECCV'00) - volume 2, number 1843 in Lecture Notes in Computer Science, pages 3- 19, Dublin, Ireland.
  10. Poli, R. (2007). An analysis of publications on particle swarm optimisation applications. Technical Report CSM-649, University of Essex, Department of Computer Science.
  11. Poli, R., Kennedy, J., Blackwell, T., and Freitas, A. (2008). Editorial for particle swarms: The second decade. Journal of Artificial Evolution and Applications, 1(1):1-3.
  12. Poppe, R. (2007). Vision-based human motion analysis: An overview. Computer Vision and Image Understanding (CVIU), 108(1-2):4-18.
  13. Robertson, C. and Trucco, E. (2006). Human body posture via hierarchical evolutionary optimization. In In: BMVC06. III:999.
  14. Robertson, C., Trucco, E., and Ivekovic, S. (2005). Dynamic body posture tracking using evolutionary optimisation. Electronics Letters, 41:1370-1371.
  15. Shi, Y. H. and Eberhart, R. C. (1998). A modified particle swarm optimizer. In Proceedings of the IEEE International Conference on Evolutionary Computation, pages 69 - 73.
  16. Sminchisescu, C. and Triggs, B. (2003). Estimating articulated human motion with covariance scaled sampling. International Journal of Robotic Research, 22(6):371-392.
  17. Starck, J. and Hilton, A. (2007). Surface capture for performance based animation. IEEE Computer Graphics and Applications, 27(3):21-31.
  18. Vondrak, M., Sigal, L., and Jenkins, O. C. (2008). Physical simulation for probabilistic motion tracking. In Proceedings of CVPR 2008, pages 1-8.
  19. Zhang, X., Hu, W., Maybank, S., Li, X., and Zhu, M. (2008). Sequential particle swarm optimization for visual tracking. In Proceedings of CVPR 2008.
Download


Paper Citation


in Harvard Style

John V., Ivekovic S. and Trucco E. (2009). ARTICULATED HUMAN MOTION TRACKING WITH HPSO . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 531-538. DOI: 10.5220/0001804505310538


in Bibtex Style

@conference{visapp09,
author={Vijay John and Spela Ivekovic and Emanuele Trucco},
title={ARTICULATED HUMAN MOTION TRACKING WITH HPSO},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={531-538},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001804505310538},
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 1: VISAPP, (VISIGRAPP 2009)
TI - ARTICULATED HUMAN MOTION TRACKING WITH HPSO
SN - 978-989-8111-69-2
AU - John V.
AU - Ivekovic S.
AU - Trucco E.
PY - 2009
SP - 531
EP - 538
DO - 10.5220/0001804505310538