Tracking Subpixel Targets with Critically Sampled Optics
Abstract
In many remote sensing applications, the area of a scene sensed by a single pixel can often be measured
in squared meters. This means that many objects of interest in a scene are smaller than a single pixel in
the resulting image. Current tracking methods rely on robust object detection using multi-pixel features.
A subpixel object does not provide enough information for these methods to work. This paper presents a
method for tracking subpixel objects in image sequences captured from a stationary sensor that is critically
sampled. Using template matching, we make a Maximum a Posteriori estimate of the target state over a
sequence of images. A distance transform is used to calculate the motion prior in linear time, dramatically
decreasing computation requirements. We compare the results of this method to a track-before-detect particle
filter designed for tracking small, low contrast objects using both synthetic and real-world imagery. Results
show our method produces more accurate state estimates and higher detection rates than the current state of
the art methods at signal-to-noise ratios as low as 3dB.
Description
ICPRAM2013 - International Conference on Pattern Recognition Applications and Methods
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.Collections
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