Multispectral detection and tracking of multiple moving targets in cluttered urban environments

Casey D. Demars; Michael C. Roggemann; Timothy C. Havens

doi:10.1117/1.OE.54.12.123106

14 December 2015 Multispectral detection and tracking of multiple moving targets in cluttered urban environments

Casey D. Demars, Michael C. Roggemann, Timothy C. Havens

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Optical Engineering, Vol. 54, Issue 12, 123106 (December 2015). https://doi.org/10.1117/1.OE.54.12.123106

Abstract

This paper presents an algorithm for target detection and tracking by fusion of multispectral imagery. In all spectral bands, we build a background model of the pixel intensities using a Gaussian mixture model, and pixels not belonging to the model are classified as foreground pixels. Foreground pixels from the spectral bands are weighted and summed into a single foreground map and filtered to give the fused foreground map. Foreground pixels are grouped into target candidates and associated with targets from a tracking database by matching features from the scale-invariant feature transform. The performance of our algorithm was evaluated with a synthetically generated data set of visible, near-infrared, midwave infrared, and long-wave infrared video sequences. With a fused combination of the spectral bands, the proposed algorithm lowers the false alarm rate while maintaining high detection rates. All 12 vehicles were tracked throughout the sequence, with one instance of a lost track that was later recovered.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Casey D. Demars, Michael C. Roggemann, and Timothy C. Havens "Multispectral detection and tracking of multiple moving targets in cluttered urban environments," Optical Engineering 54(12), 123106 (14 December 2015). https://doi.org/10.1117/1.OE.54.12.123106

Published: 14 December 2015

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