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Proceedings Paper

Intrackability theory and application
Author(s): Zheng Li; Haifeng Gong; Nong Sang; Gengming Zhua
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Paper Abstract

Many vision tasks can be posed as Bayesian inference, and the entropy of the posterior probability is a measure for uncertainty of perception, imperceptibility. In this paper, we studied the imperceptibility of multiple object tracking, intrackability. Entropy theory and Bayesian framework are used to represent multiple objects intrackability. Intrackability is computed by different kinds of tracking features. Feature selection is crucial for intrackability computation. An example of umbrellas tracking is shown in this paper. The intrackability which is computed by appearance and shape feature is compared. At last, we use intrackability to guide one application--Automatic grouping. Objects are dynamically merged and tracked as a group when they come close to each other. Automatic grouping reduces the representation when some details can't be perceived. After the intrackable part of the representation is discarded, the computation is reduced.

Paper Details

Date Published: 15 November 2007
PDF: 6 pages
Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67863I (15 November 2007); doi: 10.1117/12.750071
Show Author Affiliations
Zheng Li, Huazhong Univ. of Science and Technology (China)
Haifeng Gong, Institute of Automation (China)
Nong Sang, Huazhong Univ. of Science and Technology (China)
Gengming Zhua, Hunan Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 6786:
MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition
Tianxu Zhang; Tianxu Zhang; Carl Anthony Nardell; Carl Anthony Nardell; Hanqing Lu; Duane D. Smith; Hangqing Lu, Editor(s)

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