Precision tracking of overlapping small targets

The paper presents a Kalman filter based centroid tracking approach for multiple small targets. Before targets overlap, each target is tracked by a Kalman filter. When two targets overlap, a merged target is formed, and a Kalman filter for the overlapping target can be used to derive position and velocity of individual target. The main contributions of the paper are: 1) adaptive estimate of noise mean and variance in homogeneous and nonhomogeneous regions respectively, which can preserve SNR as much as possible. 2) Image intensity transformation for significantly reducing singular phenomena in centroid calculation, especially in a low SNR environment. 3) Robust estimate of Kalman filter parameters. Finally, experiments are conducted on images with real background and synthetic targets, a Monte Carlo analysis is directed