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Precision Tracking Based on Segmentation with Optimal Layering for Imaging Sensors
February 1995 (vol. 17 no. 2)
pp. 182-188

Abstract— In our previous work [5], we 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 [5], 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.

[1] Y. Bar-Shalom,IMDAT - Image Data Association Tracker 3.0, Interactive Software, 1993.
[2] Y. Bar-Shalom and C.R. Li,Multitarget-Multisensor Tracking: Principles and Techniques, YBS Publishing, Storrs, Conn., 1995.
[3] Y. Bar-Shalom,H.M. Shertukde,, and K.R. Pattipati,“Use of measurements from an imaging sensor for precision target tracking,” IEEE Trans. on Aerospace and Electronic Systems, vol. 25, pp. 863-871, November 1989.
[4] A. Kumar,Y. Bar-Shalom,, and E. Oron,“Image segmentation based on optimal layering for precision tracking,” DIMACS Workshop Proc., 1994.
[5] E. Oron,A. Kumar,, and Y. Bar-Shalom,“Precision tracking with segmentation for imaging sensors,” IEEE Trans. on Aerospace and Electronic Systems, vol. 29, pp. 977-987, July 1993.
[6] D.R. VanRheeden and R.A. Jones,“Noise effects on centroid tracker aim point estimation,” IEEE Trans. on Aerospace and Electronic Systems, vol. 24, March 1988.
[7] J. Zupan,Clustering of Large Data Sets, Academic Press, 1973.

Index Terms:
Image tracking, segmentation, clustering, imaging sensors, probabilistic data association, optimal layering, centroid estimation.
Citation:
Anil Kumar, Yaakov Bar-Shalom, Eliezer Oron, "Precision Tracking Based on Segmentation with Optimal Layering for Imaging Sensors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 2, pp. 182-188, Feb. 1995, doi:10.1109/34.368171
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