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This paper describes an airborne vision system that is capable of determining whether an object is moving or stationary in an outdoor environment. The proposed method, coined the Triangle Closure Method (TCM), achieves this goal by computing the aircraft's egomotion and combining it with information about the directions connecting the object and the UAS, and the expansion of the object in the image. TCM discriminates between stationary and moving objects with an accuracy rate of up to 96%. The performance of the method is validated in outdoor field tests by implementation in real-time on a quadrotor UAS. We demonstrate that the performance of TCM is better than that of a traditional background subtraction technique, as well as a method that employs the Epipolar Constraint Method. Unlike background subtraction, TCM does not generate false alarms due to parallax when a stationary object is at a distance other than that of the background. It also prevents false negatives when the object is moving along an epipolar constraint. TCM is a reliable and computationally efficient scheme for detecting moving objects, which provides an additional safety layer for autonomous navigation

TCM: A Vision-Based Algorithm for Distinguishing between Stationary and Moving Objects Irrespective of Depth Contrast from a UAS