Precision tracking based on segmentation with optimal layering forimaging sensors
Kumar, A.
Bar-Shalom, Y.
Oron, E.
Connecticut Univ., Storrs, CT;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Feb 1995
Volume: 17,
Issue: 2
On page(s): 182-188
ISSN: 0162-8828
References Cited: 7
CODEN: ITPIDJ
INSPEC Accession Number: 4887740
Digital Object Identifier: 10.1109/34.368171
Current Version Published: 2002-08-06
Abstract
In the authors' previous work Oron, Kumar, and Bar-Shalom (1993),
they presented a method for precision tracking of a low observable
target based on data obtained from imaging sensors. The image was
divided into several layers of gray level intensities and thresholded. A
binary image was obtained and grouped into clusters using image
segmentation techniques. Using the centroid measurements of the
clusters, the probabilistic data association filter (PDAF) was employed
for tracking the target centroid. In this correspondence, the division
of the image into several layers of gray level intensities is optimized
by minimizing the Bayes risk. This optimal layering of the image has the
following properties: (1) following the segmentation, a closed-form
analytical expression is obtained for the noise variance of the centroid
measurement based on a single frame; (2) in comparison to the previous
paper, the measurement noise variance is smaller by at least a factor of
2, thus improving the performance of the tracker. The usefulness of the
method for practical applications is demonstrated by considering a
sequence of real target images (a moving car) of about 20 pixels in size
in a noisy urban environment where the measurement noise was calculated
as having 0.32 pixel RMS value. Filtering with the PDAF further reduces
this by a factor of 1.6
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