A FEATURE GUIDED PARTICLE FILTER FOR ROBUST HAND TRACKING

Matti-Antero Okkonen, Janne Heikkilä, Matti Pietikäinen

2008

Abstract

Particle filtering offers an interesting framework for visual tracking. Unlike the Kalman filter, particle filters can deal with non-linear and non-Gaussian problems, which makes them suitable for visual tracking in presence of real-life disturbance factors, such as background clutter and movement, fast and unpredictable object movement and unideal illumination conditions. This paper presents a robust hand tracking particle filter algorithm which exploits the principle of importance sampling with a novel proposal distribution. The proposal distribution is based on effectively calculated color blob features, propagating the particles robustly through time even in unideal conditions. In addition, a novel method for conditional color model adaptation is proposed. The experiments show that using these methods in the particle filtering framework enables hand tracking with fast movements under real world conditions.

References

  1. Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 50(2):174-188.
  2. Bretzner, L., Laptev, I., and Lindeberg, T. (2002). Hand gesture recognition using multi-scale colour features, hierarchical models and particle filtering. In Proc. of FGR, pages 423-428.
  3. Bretzner, L. and Lindeberg, T. (1998). Feature tracking with automatic selection of spatial scales. Computer Vision and Image Understanding: CVIU, 71(3):385-392.
  4. Isard, M. and Blake, A. (1996). Contour tracking by stochastic propagation of conditional density. In Proc. of ECCV (1), pages 343-356.
  5. Leonardis, A., Bischof, H., and Pinz, A. (2006). Surf: Speeded up robust features. In Proc. of ECCV (1), volume 3951 of Lecture Notes in Computer Science. Springer.
  6. Lin, J. Y., Wu, Y., and Huang, T. S. (2004). 3d model-based hand tracking using stochastic direct search method. In Proc. of FGR, pages 693-698.
  7. Mahmoudi, F. and Parviz, M. (2006). Visual hand tracking algorithms. In Geometric Modeling and ImagingNew Trends, pages 228 - 232.
  8. Pantrigo, J. J., Montemayor, A. S., and Cabido, R. (2005a). Scatter search particle filter for 2d real-time hands and face tracking. In Proc. of ICIAP, pages 953-960.
  9. Pantrigo, J. J., Montemayor, A. S., and Sanchez, A. (2005b). Local search particle filter applied to humancomputer interaction. In Proc. of ISPA, pages 279 - 284.
  10. Pavlovic, V., Sharma, R., and Huang, T. S. (1997). Visual interpretation of hand gestures for human-computer interaction: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):677-695.
  11. Pérez, P., Vermaak, J., and Blake, A. (2004). Data fusion for visual tracking with particles. Proceedings of the IEEE (issue on State Estimation), 92:495 - 513.
  12. Rehg, J. M. and Kanade, T. (1993). Digiteyes: Vision-based human hand tracking. Technical report, Carnegie Mellon University, Pittsburgh, PA, USA.
  13. Shan, C., Wei, Y., Tan, T., and Ojardias, F. (2004). Real time hand tracking by combining particle filtering and mean shift. In Proc. of FGR, pages 669-674.
  14. Swain, M. J. and Ballard, D. H. (1991). Color indexing. Int. J. Comput. Vision, 7(1):11-32.
  15. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proc. of CVPR.
Download


Paper Citation


in Harvard Style

Okkonen M., Heikkilä J. and Pietikäinen M. (2008). A FEATURE GUIDED PARTICLE FILTER FOR ROBUST HAND TRACKING . 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 368-374. DOI: 10.5220/0001078503680374


in Bibtex Style

@conference{visapp08,
author={Matti-Antero Okkonen and Janne Heikkilä and Matti Pietikäinen},
title={A FEATURE GUIDED PARTICLE FILTER FOR ROBUST HAND TRACKING},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={368-374},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001078503680374},
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 - A FEATURE GUIDED PARTICLE FILTER FOR ROBUST HAND TRACKING
SN - 978-989-8111-21-0
AU - Okkonen M.
AU - Heikkilä J.
AU - Pietikäinen M.
PY - 2008
SP - 368
EP - 374
DO - 10.5220/0001078503680374