Authors:
James Lotspeich
and
Mathias Kolsch
Affiliation:
Naval Postgraduate School, United States
Keyword(s):
Subpixel, Maximum a Posterior, Tracking, Viterbi, Distance Transform, Pixel Matched Filter, Template Matching.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Gaussian Processes
;
Graphical and Graph-Based Models
;
Human-Computer Interaction
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Theory and Methods
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 sta
te estimates and higher detection rates than the current state of the art methods at signal-to-noise ratios as low as 3dB.
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