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Optical Engineering • Open Access

Particle probability hypothesis density filtering for multitarget visual tracking with robust state extraction
Author(s): Jingjing Wu; Shiqiang Hu; Yang Wang

Paper Abstract

Particle probability hypothesis density (PHD) filter-based visual trackers have achieved considerable success in the visual tracking field. But position measurements based on detection may not have enough ability to discriminate an object from clutter, and accurate state extraction cannot be obtained in the original PHD filtering framework, especially when targets can appear, disappear, merge, or split at any time. To meet the limitations, the proposed algorithm combines a color histogram of a target and the temporal dynamics in a unifying framework and a Gaussian mixture model clustering method for efficient state extraction is designed. The proposed tracker can improve the accuracy of state estimation in tracking a variable number of objects.

Paper Details

Date Published: 1 September 2011
PDF: 4 pages
Opt. Eng. 50(9) 090502 doi: 10.1117/1.3638121
Published in: Optical Engineering Volume 50, Issue 9
Show Author Affiliations
Jingjing Wu, Shanghai Jiao Tong Univ. (China)
Shiqiang Hu, Shanghai Jiao Tong Univ. (China)
Yang Wang, Shanghai Jiao Tong Univ. (China)

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