Abstract
Mean shift is an efficient pattern match algorithm. It is widely used in visual tracking fields since it need not perform whole search in the image space. It employs gradient optimization method to reduce the time of feature matching and realize rapid object localization, and uses Bhattacharyya coefficient as the similarity measure between object template and candidate template. This thesis presents a mean shift algorithm based on coarse-to-fine search for the best kernel matching. This paper researches for object tracking with large motion area based on mean shift. To realize efficient tracking of such an object, we present a kernel matching method from coarseness to fine. If the motion areas of the object between two frames are very large and they are not overlapped in image space, then the traditional mean shift method can only obtain local optimal value by iterative computing in the old object window area, so the real tracking position cannot be obtained and the object tracking will be disabled. Our proposed algorithm can efficiently use a similarity measure function to realize the rough location of motion object, then use mean shift method to obtain the accurate local optimal value by iterative computing, which successfully realizes object tracking with large motion. Experimental results show its good performance in accuracy and speed when compared with background-weighted histogram algorithm in the literature.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
C. Dongyan, Z. Qi, “Implementation of maneuver target prediction in image tracking system”, Appl. optics 28, 33–37 (2007).
Y. Zhu and K. Fujimura, “Driver face tracking using Gaussian mixture model (GMM)”, IEEE Intelligent Vehicles Symposium 4, 587–592 (2003).
M.I. Khan and A.B. Mansoor, “Real time eyes tracking and classification for driver fatigue detection”, Lect. Notes. Comput. Sc. 5112, 729–738 (2008).
H. Rile, Z. Chengqing, and X. Bo, “An approach to automatic acquisition of translation templates based on phrase structure extraction and alignment”, IEEE T. Audio Speech 14, 1656–1663 (2006).
B. Schuller and M. Wollmer, “Switching linear dynamic models for noise robust in-car speech recognition”, Pattern Recogn. 5096, 244–253 (2008).
S. Cekli, E. Cekli, N. Kabaoglu, and H.A. Cirpan, “EM-based recursive tracking algorithm for near-field moving sources”, IEEE Intelligent Vehicles Symposium 4, 381–385 (2007).
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).
K. Nummiaro, E. Koller-Meier, and L. Van Gool, “An adaptive colour-based particle filter”, Image Vision Comput. 21, 99–110 (2003).
G.D. Hager, M. Dewan, and C.V. Stewart, “Multiple kernel tracking with SSD”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1, 790–797 (2004).
D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis”, IEEE T. Pattern Anal. 24, 603–619 (2002).
D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking”, IEEE T. Pattern Anal. 25, 564–577 (2003).
J.R. Jimenez, V. Medina, and O. Yanez, “Nonparametric MRI segmentation using mean shift and edge confidence maps”, Proc. SPIE 5032, 1433–1441 (2003).
Author information
Authors and Affiliations
About this article
Cite this article
Liangfu, L., Zuren, F., Weidong, C. et al. A coarse-to-fine kernel matching approach for mean-shift based visual tracking. Opto-Electron. Rev. 17, 65–71 (2009). https://doi.org/10.2478/s11772-008-0058-4
Published:
Issue Date:
DOI: https://doi.org/10.2478/s11772-008-0058-4