PRECISION TRACKING BASED ON SEGMENTATION WITH OPTIMAL LAYERING FOR IMAGING SENSORS

In our previous work [5], we presented a method for precision tracking of a low observable target based on data obtained from imaging sensor s. The image was divided into several layers of gray level intensities and thresholded. A binary image was obtained and grouped into cluster s 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, th e division of the image into several layers of gray level intensities is optimized by minimizing the Bayes risk. This optimal layering of th e 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 o f 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 cal culated as having 0.32 pixel RMS value. Filtering with the PDAF furthe r reduces this by a factor of 1.6.