A statistically based tracking algorithm is described which utilizes a powerful segmentation algorithm. Multiple features such as intensity, edge magnitude, and spatial frequency are combined to form a joint probability distribution to characterize a region containing a target and its immediate surround. These distributions are integrated over time to provide a stable estimate of the target region and background statistics. A Bayesian decision rule is implemented using these distributions to classify individual pixels as target or nontarget. An adaptive gate process is used to estimate desired changes in the tracking window size.
W. B. Schaming,
"Adaptive Gate Multifeature Bayesian Statistical Tracker", Proc. SPIE 0359, Applications of Digital Image Processing IV, (17 March 1983); doi: 10.1117/12.965948; https://doi.org/10.1117/12.965948