Biologically Inspired Tracking of Small Moving Targets
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Flies have the capability to visually track small moving targets, even across cluttered backgrounds. Previous computational models, based on Figure Detection (FD) cells identified in the fly, have suggested how this may be accomplished at a neuronal level based on information about relative motion between the target and the background.  We have experimented with the use of this ``small-field system model'' for the tracking of small moving targets by a simulated fly in a cluttered environment, and discovered some functional limitations.  As a result of these experiments, we have proposed elaborations of the original small-field system model to support stronger effects of background motion on small-field responses, proper accounting for more complex optical flow fields, and more direct guidance toward the target. The elaborated model achieves much better tracking performance than the original model in complex visual environments, and may help to explain recent electrophysiological data on FD cells which seems to contradict the original model. Taking inspiration from this biological model, we have designed and fabricated a monolithic analog VLSI sensor which produces control signals appropriate for the guidance of an autonomous robot to visually track a small moving target.  This sensor is specifically designed to allow such tracking even from a moving imaging platform which experiences complex background optical flow patterns.