Multiresolution motion estimation and target detection

Robert E. Karlsen; David J. Gorsich; Grant R. Gerhart

doi:10.1117/12.327150

7 July 1998 Multiresolution motion estimation and target detection

Robert E. Karlsen, David J. Gorsich, Grant R. Gerhart

Proceedings Volume 3375, Targets and Backgrounds: Characterization and Representation IV; (1998) https://doi.org/10.1117/12.327150
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

Detecting and characterizing motion in a scene can play a critical role in target detection algorithms, since many targets can be camouflaged so completely that, if they are not moving, they are nearly undetectable. However, once they begin moving, they `popout' and are immediately detected. Estimating motion is also important in human vision modeling, because motion is generally detected with peripheral vision, which can cover the field of regard much more quickly than foveal vision. In this paper, we present two hierarchical multiresolution methods for computing the optical flow in a scene. We use statistical properties of the resulting flow fields to compute a motion feature vector, which we relate to the conspicuity of the moving target in a scene via a neural network.

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Robert E. Karlsen, David J. Gorsich, and Grant R. Gerhart "Multiresolution motion estimation and target detection", Proc. SPIE 3375, Targets and Backgrounds: Characterization and Representation IV, (7 July 1998); https://doi.org/10.1117/12.327150

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KEYWORDS

Optical flow

Neural networks

Target detection

Motion estimation

Motion detection

Neurons

Detection and tracking algorithms

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