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

Vamsi Kilaru; Moeness G. Amin; Fauzia Ahmad; Pascale Sévigny; David D. J. DiFilippo

doi:10.1117/1.JEI.24.1.013028

17 February 2015 Gaussian mixture modeling approach for stationary human identification in through-the-wall radar imagery

Vamsi Kilaru, Moeness G. Amin, Fauzia Ahmad, Pascale Sévigny, David D. J. DiFilippo

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 1, 013028 (February 2015). https://doi.org/10.1117/1.JEI.24.1.013028

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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Vamsi Kilaru, Moeness G. Amin, Fauzia Ahmad, Pascale Sévigny, and David D. J. DiFilippo "Gaussian mixture modeling approach for stationary human identification in through-the-wall radar imagery," Journal of Electronic Imaging 24(1), 013028 (17 February 2015). https://doi.org/10.1117/1.JEI.24.1.013028

Published: 17 February 2015

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