1. A method for tracking an object within a dynamic image, comprising: receiving a first image including the object; generating a posterior probability density function representing the location of the object within the first image; automatically detecting information pertaining to the modes of the posterior probability density function and using the detected information pertaining to the modes to determine a number of sets of Gaussian components with which to approximate a predicted probability density function representing the location of the object within a subsequent image; evolving the modes of the posterior probability density function from a previous measurement frame into a set of modes of the posterior probability density function of a current measurement frame, wherein evolving the modes of the posterior probability density function including the steps of: detecting information pertaining to modes of the predicted probability density function; detecting information pertaining to modes of a likelihood function of the current measurement frame; and detecting information pertaining to the modes of the posterior probability density function of the current measurement frame using the information of the modes of the predicted probability density function and the information of the modes of the likelihood function; generating an analytic expression for the predicted probability density function based on the evolved set of modes of the posterior probability density function using the determined number of sets of Gaussian components; and using the generated analytic expression of the predicted probability density function to track the location of the object within the subsequent image.