Precision tracking based on segmentation with optimal layering for imaging sensors

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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