KERNEL SELECTION BY MUTUAL INFORMATION FOR NONPARAMETRIC OBJECT TRACKING

J. M. Berthommé, T. Chateau, M. Dhome

2012

Abstract

This paper presents a method to select kernels for the subsampling of nonparametric models used in realtime object tracking in video streams. We propose a method based on mutual information, inspired by the CMIM algorithm (Fleuret, 2004) for the selection of binary features. This builds, incrementally, a model of appearance of the object to follow, based on representative and independant kernels taken from points of that object. Experiments show gains, in terms of accuracy, compared to other sampling strategies.

References

  1. Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. Signal Processing, IEEE Transactions on, 50(2):174-188.
  2. Boltz, S., Debreuve, E., and Barlaud, M. (2009). Highdimensional statistical measure for region-of-interest tracking. IEEE Transactions on Image Processing, 18(6):1266-1283.
  3. Comaniciu, D., Ramesh, V., and Meer, P. (2000). Realtime tracking of non-rigid objects using mean shift. In CVPR, volume 2, pages 142-149. Published by the IEEE Computer Society.
  4. Fleuret, F. (2004). Fast binary feature selection with conditional mutual information. The Journal of Machine Learning Research, 5:1531-1555.
  5. Fukunaga, K. and Hostetler, L. (1975). The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 21(1):32-40.
  6. Garcia, V., Debreuve, E., and Barlaud, M. (2008). Fast k nearest neighbor search using GPU. In CVPR Workshop on Computer Vision on GPU (CVGPU), Anchorage, Alaska, USA.
  7. Khan, Z., Balch, T., and Dellaert, F. (2005). Mcmc-based particle filtering for tracking a variable number of interacting targets. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1805-1918.
  8. MacKay, D. (2003). Information Theory, Inference, and Learning Algorithms. Cambridge University Press.
  9. Parzen, E. (1962). On the estimation of a probability density function and mode. The Annals of Mathematical Statistics, 33(3):1065-1076.
Download


Paper Citation


in Harvard Style

M. Berthommé J., Chateau T. and Dhome M. (2012). KERNEL SELECTION BY MUTUAL INFORMATION FOR NONPARAMETRIC OBJECT TRACKING . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-04-4, pages 373-376. DOI: 10.5220/0003833603730376


in Bibtex Style

@conference{visapp12,
author={J. M. Berthommé and T. Chateau and M. Dhome},
title={KERNEL SELECTION BY MUTUAL INFORMATION FOR NONPARAMETRIC OBJECT TRACKING},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={373-376},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003833603730376},
isbn={978-989-8565-04-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012)
TI - KERNEL SELECTION BY MUTUAL INFORMATION FOR NONPARAMETRIC OBJECT TRACKING
SN - 978-989-8565-04-4
AU - M. Berthommé J.
AU - Chateau T.
AU - Dhome M.
PY - 2012
SP - 373
EP - 376
DO - 10.5220/0003833603730376