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Proceedings Paper

On the limits of target recognition in the presence of atmospheric effects
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Paper Abstract

The importance of Networked Automatic Target Recognition systems for surveillance applications is continuously increasing. Because of the requirement of a low cost and limited payload these networks are traditionally equipped with lightweight, low-cost sensors such as Electro Optical or Infrared sensors. The quality of imagery acquired by these sensors critically depends on the environmental conditions, type and characteristics of sensors, and absence of occluding or concealing objects. In the past a large number of efficient detection, tracking, and recognition algorithms have been designed to operate on imagery of good quality. However, detection and recognition limits under non-ideal environmental and/or sensor based distortions have not been carefully evaluated. This work describes a real image dataset formed by imaging 10 die cast models of military vehicles at different elevation and orientation angles. The dataset contains imagery acquired both indoors and outdoors. The indoors dataset is composed of clear and distorted images. The distortions include defocus blur, sided illumination, low contrast, shadows and occlusions. All images in this dataset, however, have a uniform blue background. The indoors dataset is applied to evaluate the degradations of recognition performance due to camera and illumination effects. The recognition method is based on Bessel K forms. The dataset collected outdoors includes real background and is much more complex to process. This dataset is used to evaluate performance of a fully automatic target recognition system that involves a Haar-based detector to select potential regions of interest within images; performs adjustment and fusion of detected regions; segments potential targets using a region based approach; identifies targets using Bessel K form-based encoding; and performs clutter rejection. The numerical results demonstrate that the complexity of the background and the presence of occlusions lead to substantial detection and recognition performance degradations.

Paper Details

Date Published: 17 April 2008
PDF: 12 pages
Proc. SPIE 6968, Signal Processing, Sensor Fusion, and Target Recognition XVII, 69680J (17 April 2008);
Show Author Affiliations
Xiaohan Chen, West Virginia Univ. (United States)
Natalia A. Schmid, West Virginia Univ. (United States)

Published in SPIE Proceedings Vol. 6968:
Signal Processing, Sensor Fusion, and Target Recognition XVII
Ivan Kadar, Editor(s)

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