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Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Editor: Franco Gori
  • Vol. 28, Iss. 9 — Sep. 1, 2011
  • pp: 1935–1940

Tracking of multiple objects in unknown background using Bayesian estimation in 3D space

Yige Zhao, Xiao Xiao, Myungjin Cho, and Bahram Javidi  »View Author Affiliations

JOSA A, Vol. 28, Issue 9, pp. 1935-1940 (2011)

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We present a three-dimensional (3D) object tracking method based on a Bayesian framework for tracking multiple, occluded objects in a complex scene. The 3D passive capture of scene data is based on integral imaging. The statistical characteristics of the objects versus the background are exploited to analyze each frame. The algorithm can work with objects with unknown position, rotation, scale, and illumination. Posterior probabilities of the reconstructed scene background and the 3D objects are calculated by defining their pixel intensities as Gaussian and gamma distributions, respectively, and by assuming appropriate prior distributions for estimated parameters. Multiobject tracking is achieved by maximizing the geodesic distance between the log-posteriors of the background and the objects. Experimental results are presented.

© 2011 Optical Society of America

OCIS Codes
(110.6880) Imaging systems : Three-dimensional image acquisition
(150.6910) Machine vision : Three-dimensional sensing
(280.4991) Remote sensing and sensors : Passive remote sensing

ToC Category:
Imaging Systems

Original Manuscript: January 24, 2011
Revised Manuscript: July 13, 2011
Manuscript Accepted: July 14, 2011
Published: August 29, 2011

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February 13, 2012 Spotlight on Optics

Yige Zhao, Xiao Xiao, Myungjin Cho, and Bahram Javidi, "Tracking of multiple objects in unknown background using Bayesian estimation in 3D space," J. Opt. Soc. Am. A 28, 1935-1940 (2011)

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