An efficient object tracking method based on adaptive nonparametric approach

• Autorzy: Li L. , Feng Z.
• Języki publikacji: EN

Abstrakty

EN

In this paper, an efficient method for object tracking based on nonparametric approach is presented. The density we estimated is based on an adaptive kernel model, which is driven by the intensity difference between the target and the background. The background-weighted histogram for statistics of feature takes into account the relevance between the target and background. What is more, this approach extends the range that is needed for searching object. The target model is updated according to the change of the object and environment. Experimental results on real image sequences demonstrate its robust performance in visual tracking and require less iteration computations when compared to other method.

Słowa kluczowe

EN

object tracking; adaptive; nonparametric; update; kernel

• Wydawca: Stowarzyszenie Elektryków Polskich ; Polska Akademia Nauk ; Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
• Czasopismo: Opto-Electronics Review
• Rocznik: 2005
• Tom: Vol. 13, No. 4
• Strony: 325--330
• Opis fizyczny: Bibliogr. 11 poz., il., wykr.

Twórcy

autor

Li L.

• Institute of System Engineering, Xi'an Jiaotong University, Xi'an, ShanXi, 710049 P.R. China

autor

Feng Z.

• Institute of System Engineering, Xi'an Jiaotong University, Xi'an, ShanXi, 710049 P.R. China

Bibliografia

• 1. W. Ying, "Robust visual tracking by integrating multiple cues based on co-inference learning", International Journal on Computer Vision 58, 55-71 (2004).
• 2. M. Isard and A. Blake, "Condensation - conditional density propagation for visual tracking", International Journal on Computer Vision 29, 5-28 (1998).
• 3. A. Logothetis, V. Krishnamurthy, and J. Holst, "A Bayesian EM algorithm for optimal tracking of a maneuvering target in clutter", Signal Processing 82, 473-490 (2002).
• 4. J.R. Jimenez, V. Medina, and O. Yanez, "Nonparametric MRI segmentation using mean shift and edge confidence maps", Proc. SPIE 5032, 1433-1441 (2003).
• 5. D. Comaniciu, V. Ramesh, and P. Meer, "Real-time tracking of non-rigid objects using mean shift", Computer Vision and Pattern Recognition 2, 142-149 (2000).
• 6. K. Nummiaro, E. Koller-Meier, and L. Van Gool, "An adaptive colour-based particle filter", Image and Vision Computing 21, 99-110 (2003).
• 7. G.D. Hager, M. Dewan, and C.V. Stewart, "Multiple kernel tracking with SSD", IEEE Computer Society Conference on Computer Vision and Pattern Recognition, I-790-I-797 (2004).
• 8. D. Comaniciu and P. Meer, "Mean shift: a robust approach toward feature space analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603-619 (2002).
• 9. D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking", IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 564-577 (2003).
• 10. M.A. King, T.K. Lee, M.S. Atkins, and D.I. McLean, "Automatic nevi segmentation using adaptive mean shift filters and feature analysis", Proc. SPIE 5370, 1730-1737 (2004).
• 11. E. Arnaud and E. Memin, "Optimal importance sampling for tracking in image sequences: application to point tracking", ECCV 2004, LNCS 3023, 302-314 (2004).