Precision tracking with segmentation for imaging sensors

Precision target tracking based on data obtained from imaging sensors when the target is not fully visible during tracking is addressed. The image is divided into several layers of gray level intensities and thresholded. A binary image is obtained and grouped into clusters using image segmentation. The association of the various clusters to the track to be estimated relies on both the motion and pattern recognition characteristics of the target. The centroid measurements of the clusters and the probabilistic data association filter (PDAF) are employed for state estimation. Expressions for the single-frame-based centroid measurement noise variance of the target cluster and the optimal parameters for cluster segmentation are given. Simulation results validate the expressions for the measurement noise variance as well as the performance predictions of the tracking method. For a dim synthetic target with strong background noise, subpixel accuracy in the range of 0.3-0.4 pixel RMS error with moderate (0.7) to low (0.3) target pixel detection probability was achieved. The subpixel detection probability can be further improved for a larger target. The usefulness of the method for practical applications is demonstrated for a sequence of real target images