Tracking Subpixel Targets with Critically Sampled Optical Sensors
dc.contributor.advisor | Kolsch, Mathias | |
dc.contributor.author | Lotspeich, James T | |
dc.date | Sep-12 | |
dc.date.accessioned | 2012-11-14T00:02:47Z | |
dc.date.available | 2012-11-14T00:02:47Z | |
dc.date.issued | 2012-09 | |
dc.identifier.uri | https://hdl.handle.net/10945/17407 | |
dc.description.abstract | In many remote sensing applications, the area of a scene sensed by a single pixel can often be measured in square 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 dissertation presents a method for tracking subpixel objects in image sequences captured from a stationary sensor that is critically sampled spatially. Using template matching, we estimate the maximum a posteriori probability 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 previously state-of-the-art 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. | en_US |
dc.description.uri | http://archive.org/details/trackingsubpixel1094517407 | |
dc.publisher | Monterey, California. Naval Postgraduate School | en_US |
dc.title | Tracking Subpixel Targets with Critically Sampled Optical Sensors | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Computer Science | |
dc.subject.author | Subpixel, Tracking, Hidden Markov Model, Viterbi, Distance Transform | en_US |
dc.description.service | Major, United States Air Force | en_US |
etd.thesisdegree.name | Ph.D in Computer Science | en_US |
etd.thesisdegree.level | Doctoral | en_US |
etd.thesisdegree.discipline | Computer Science | en_US |
dc.description.distributionstatement | Approved for public release; distribution is unlimited. |
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