JEI_22_4_041115[1].pdf (5.64 MB)

Human object annotation for surveillance video forensics

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journal contribution
posted on 29.11.2013, 14:10 by Fraz Fraz, Iffat Zafar, Giounona Tzanidou, Eran Edirisinghe, Muhammad S. Sarfraz
A system that can automatically annotate surveillance video in a manner useful for locating a person with a given description of clothing is presented. Each human is annotated based on two appearance features: primary colors of clothes and the presence of text/logos on clothes. The annotation occurs after a robust foreground extraction stage employing a modified Gaussian mixture model-based approach. The proposed pipeline consists of a preprocessing stage where color appearance of an image is improved using a color constancy algorithm. In order to annotate color information for human clothes, we use the color histogram feature in HSV space and find local maxima to extract dominant colors for different parts of a segmented human object. To detect text/logos on clothes, we begin with the extraction of connected components of enhanced horizontal, vertical, and diagonal edges in the frames. These candidate regions are classified as text or nontext on the basis of their local energy-based shape histogram features. Further, to detect humans, a novel technique has been proposed that uses contourlet transform-based local binary pattern (CLBP) features. In the proposed method, we extract the uniform direction invariant LBP feature descriptor for contourlet transformed high-pass subimages from vertical and diagonal directional bands. In the final stage, extracted CLBP descriptors are classified by a trained support vector machine. Experimental results illustrate the superiority of our method on large-scale surveillance video data.

History

School

  • Science

Department

  • Computer Science

Citation

FRAZ, M. ... et al., 2013. Human object annotation for surveillance video forensics. Journal of Electronic Imaging, 22 (4), pp. 041115-1 - 041115-15.

Publisher

© Society of Photo-Optical Instrumentation Engineers

Version

VoR (Version of Record)

Publication date

2013

Notes

This article was published in the Journal of Electronic Imaging [Copyright (2013) Society of Photo-Optical Instrumentation Engineers]. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

ISSN

1017-9909

eISSN

1560-229X

Language

en

Exports

Loughborough Publications

Keywords

Exports