Enhanced Micro Target Detection through Local Motion Feedback in Biologically Inspired Algorithms

Looking for micro targets (objects in the range of 1.2×1.2 pixels) that are moving in electro-optic imagery is a relatively simple task when the background is perfectly still. Once motion is introduced into the background, such as movement from trees and bushes or ego-motion induced by a moving platform, the task becomes much more difficult. Flies have a method of dealing with such motion while still being able to detect small moving targets. This paper takes an existing model based on the fly's early visual systems and compares it to existing methods of target detection. High dynamic range imagery is used and motion induced to reflect the effects of a rotating platform. The model of the fly's visual system is then enhanced to include local area motion feedback to help separate the moving background from moving targets in cluttered scenes. This feedback increases the performance of the system, showing a general improvement of over 80% from the baseline model, and 30 times better performance than the pixel-based adaptive segmenter and local contrast methods. These results indicate the enhanced model is able to perform micro target detection with better discrimination between targets and the background in cluttered scenes from a moving platform.