A Self-adaptive Likelihood Function for Tracking with Particle Filter

Séverine Dubuisson, Myriam Robert-Seidowsky, Jonathan Fabrizio

Abstract

The particle filter is known to be efficient for visual tracking. However, its parameters are empirically fixed, depending on the target application, the video sequences and the context. In this paper, we introduce a new algorithm which automatically adjusts online two majors of them: the correction and the propagation parameters. Our purpose is to determine, for each frame of a video, the optimal value of the correction parameter and to adjust the propagation one to improve the tracking performance. On one hand, our experimental results show that the common settings of particle filter are sub-optimal. On another hand, we prove that our approach achieves a lower tracking error without needing to tune these parameters. Our adaptive method allows to track objects in complex conditions (illumination changes, cluttered background, etc.) without adding any computational cost compared to the common usage with fixed parameters.

References

  1. Bhattacharyya, A. (1943). On a measure of divergence between two statistical populations defined by their probability distributions. Bulletin of Cal. Math. Soc., 35(1):99-109.
  2. Brasnett, P. and Mihaylova, L. (2007). Sequential monte carlo tracking by fusing multiple cues in video sequences. Image and Vision Computing, 25(8):1217- 1227.
  3. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In CVPR, pages 886- 893.
  4. Erdem, E., Dubuisson, S., and Bloch, I. (2012). Visual tracking by fusing multiple cues with context-sensitive reliabilities. Pattern Recognition, 45(5):1948-1959.
  5. Fontmarty, M., Lerasle, F., and Danes, P. (2009). Likelihood tuning for particle filter in visual tracking. In ICIP, pages 4101-4104.
  6. Gordon, N., Salmond, D., and Smith, A. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc. of Radar and Signal Processing, 140(2):107-113.
  7. Hassan, W., Bangalore, N., Birch, P., Young, R., and Chatwin, C. (2012). An adaptive sample count particle filter. Computer Vision and Image Understanding, 116(12):1208-1222.
  8. Lichtenauer, J., Reinders, M., and Hendriks, E. (2004). Influence of the observation likelihood function on object tracking performance in particle filtering. In FG, pages 767-772.
  9. Maggio, E., Smerladi, F., and Cavallaro, A. (2007). Adaptive Multifeature Tracking in a Particle Filtering Framework. IEEE Transactions on Circuits and Systems for Video Technology, 17(10):1348-1359.
  10. Ng, K. K. and Delp, E. J. (2009). New models for realtime tracking using particle filtering. In VCIP, volume 7257.
  11. Pérez, P., Hue, C., Vermaak, J., and Gangnet, M. (2002). Color-Based Probabilistic Tracking. In ECCV, pages 661-675.
  12. Wu, Y., Lim, J., and Yang, M.-H. (2013). Online object tracking: A benchmark. In CVPR, pages 2411-2418.
Download


Paper Citation


in Harvard Style

Dubuisson S., Robert-Seidowsky M. and Fabrizio J. (2015). A Self-adaptive Likelihood Function for Tracking with Particle Filter . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-091-8, pages 446-453. DOI: 10.5220/0005260004460453


in Bibtex Style

@conference{visapp15,
author={Séverine Dubuisson and Myriam Robert-Seidowsky and Jonathan Fabrizio},
title={A Self-adaptive Likelihood Function for Tracking with Particle Filter},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={446-453},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005260004460453},
isbn={978-989-758-091-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - A Self-adaptive Likelihood Function for Tracking with Particle Filter
SN - 978-989-758-091-8
AU - Dubuisson S.
AU - Robert-Seidowsky M.
AU - Fabrizio J.
PY - 2015
SP - 446
EP - 453
DO - 10.5220/0005260004460453