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Gaussian mixture modeling approach for stationary human identification in through-the-wall radar imagery

[+] Author Affiliations
Vamsi Kilaru, Moeness Amin, Fauzia Ahmad

Villanova University, Center for Advanced Communications, Radar Imaging Lab, Villanova, Pennsylvania 19085, United States

Pascale Sévigny, David DiFilippo

Defence Research and Development Canada, Radar Sensing and Exploitation, Ottawa, Ontario, K1A 0Z4, Canada

J. Electron. Imaging. 24(1), 013028 (Feb 17, 2015). doi:10.1117/1.JEI.24.1.013028
History: Received September 8, 2014; Accepted January 20, 2015
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Abstract.  We propose a Gaussian mixture model (GMM)-based approach to discriminate stationary humans from their ghosts and clutter in through-the-wall radar images. More specifically, we use a mixture of Gaussian distributions to model the image intensity histograms corresponding to target and ghost/clutter regions. The mixture parameters, namely the means, variances, and weights of the component distributions, are used as features and a K-nearest neighbor classifier is employed. The performance of the proposed method is evaluated using real-data measurements of multiple humans standing or sitting at different locations in a small room. Experimental results show that the nature of the targets and ghosts/clutter in the image allows successful application of the GMM feature-based classifier to distinguish between target and ghost/clutter regions.

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Citation

Vamsi Kilaru ; Moeness Amin ; Fauzia Ahmad ; Pascale Sévigny and David DiFilippo
"Gaussian mixture modeling approach for stationary human identification in through-the-wall radar imagery", J. Electron. Imaging. 24(1), 013028 (Feb 17, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.1.013028


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