ROBUST MULTI-TARGET TRACKING USING MEAN SHIFT AND PARTICLE FILTER WITH TARGET MODEL UPDATE

Hong Liu, Jintao Li, Yueliang Qian, Qun Liu

Abstract

We propose a novel multiple targets tracking algorithm combining Mean Shift and Particle Filter, and enhance the performance with target model update process. Mean Shift has a low complexity, but is weak in dealing with multi-modal probability density functions (pdfs). Particle Filter is robust to the partial occlusion and can deal with multi-modal pdfs. In real application, illumination conditions, the visual angle as well as object occlusion can change target appearance, thus influence the quality of Particle Filter. For multi-target tracking task, the mutual occlusion of targets and computational complexity are important problems for tracking system. In this paper, Mean Shift algorithm is embedded into Particle Filter framework to get stable tracking and reduce computational load. To overcome the target appearance changes caused by illumination changes and object occlusion, targets model are updated adaptively during tracking. Experimental results show that our tracking system can robustly track multiple targets with mutual occlusion and correctly maintain their identities with smaller number of particles than Particle Filter.

References

  1. Comaniciu, D., Ramesh, V. and Meer, P., 2003, Kernelbased object tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.25, no.5, 564-577.
  2. 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, vol.50, no.2, 174-188.
  3. Maggio, E. and A. Cavallaro., 2005, Hybrid Particle Filter and Mean Shift tracker with adaptive transition model. In Acoustics, Speech, and Signal Processing, 221- 224.
  4. Yang, C.J., Duraiswami, R. and Davis, L.S., 2005, Fast Multiple Object Tracking via a Hierarchical Particle Filter. ICCV 2005, 212-219
  5. Yizheng Cai, Nando de Freitas, James J., 2006, Little: Robust Visual Tracking for Multiple Targets. ECCV 2006, 107-118
  6. Nummiaro K, Koller-Meier E, Van Gool L., 2002, Object tracking with an adaptive color-based particle filter. Symposium for Pattern Recognition of the DAGM, 353-360
  7. Shan, C., Wei, Y., Tan, T., Ojardias, F., 2004, Real Time Hand Tracking by Combining Particle Filtering and Mean Shift. In: International Conference on Automatic Face and Gesture Recognition. 669-674.
  8. http://Homepages.inf.edac.uk/rbf/CAVIAR., 2004
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Paper Citation


in Harvard Style

Liu H., Li J., Qian Y. and Liu Q. (2008). ROBUST MULTI-TARGET TRACKING USING MEAN SHIFT AND PARTICLE FILTER WITH TARGET MODEL UPDATE . 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 605-610. DOI: 10.5220/0001080506050610


in Bibtex Style

@conference{visapp08,
author={Hong Liu and Jintao Li and Yueliang Qian and Qun Liu},
title={ROBUST MULTI-TARGET TRACKING USING MEAN SHIFT AND PARTICLE FILTER WITH TARGET MODEL UPDATE},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={605-610},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001080506050610},
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 - ROBUST MULTI-TARGET TRACKING USING MEAN SHIFT AND PARTICLE FILTER WITH TARGET MODEL UPDATE
SN - 978-989-8111-21-0
AU - Liu H.
AU - Li J.
AU - Qian Y.
AU - Liu Q.
PY - 2008
SP - 605
EP - 610
DO - 10.5220/0001080506050610