Accession Number:

ADA570791

Title:

Tracking Subpixel Targets with Critically Sampled Optical Sensors

Descriptive Note:

Doctoral thesis

Corporate Author:

NAVAL POSTGRADUATE SCHOOL MONTEREY CA

Personal Author(s):

Report Date:

2012-09-01

Pagination or Media Count:

93.0

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.

Subject Categories:

  • Infrared Detection and Detectors

Distribution Statement:

APPROVED FOR PUBLIC RELEASE