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This paper presents the development of a real time tracking algorithm thatruns on a 1.2 GHz PC/104 computer on-board a small UAV. The algorithm uses zeromean normalized cross correlation to detect and locate an object in the image.A kalman filter is used to make the tracking algorithm computationallyefficient. Object position in an image frame is predicted using the motionmodel and a search window, centered at the predicted position is generated.Object position is updated with the measurement from object detection. Thedetected position is sent to the motion controller to move the gimbal so thatthe object stays at the center of the image frame. Detection and tracking isautonomously carried out on the payload computer and the system is able to workin two different methods. The first method starts detecting and tracking usinga stored image patch. The second method allows the operator on the ground toselect the interest object for the UAV to track. The system is capable ofre-detecting an object, in the event of tracking failure. Performance of thetracking system was verified both in the lab and on the field by mounting thepayload on a vehicle and simulating a flight. Tests show that the system candetect and track a diverse set of objects in real time. Flight testing of thesystem will be conducted at the next available opportunity.

On-Board Visual Tracking with Unmanned Aircraft System (UAS)