This research evaluates image motion analysis using a model- fitting estimator developed by the Principal Investigator. This estimator, which fits the unknown likelihood function with a function selected from a multi-scale family of model likelihood functions, is claimed to be highly robust, less dependent on initial guesses, and resistant to data confusion in multi-object tracking. The method has so far been tested with simulated data; tests would be expanded to real data from both laboratory and outdoor scenes, including mixed modalities with range and visual images.
