Intrackability theory and application

Zheng Li; Haifeng Gong; Nong Sang; Gengming Zhua

doi:10.1117/12.750071

15 November 2007 Intrackability theory and application

Zheng Li, Haifeng Gong, Nong Sang, Gengming Zhua

Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 67863I (2007) https://doi.org/10.1117/12.750071
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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.

Citation Download Citation

Zheng Li, Haifeng Gong, Nong Sang, and Gengming Zhua "Intrackability theory and application", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67863I (15 November 2007); https://doi.org/10.1117/12.750071

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