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In this paper, a novel method is presented for tracking ground targets from an unmanned aerial vehicle (UAV) outfitted with a single monocular camera. The loss of observability resulting from the use of a single monocular camera is dealt with by constraining the target vehicle to follow ground terrain. An unscented Kalman filter (UKF) provides a simultaneous localization and mapping solution for the estimation of aircraft states and feature locations, which define the target's local environment. Another filter, a loosely coupled Kalman filter for the target states, receives 3D measurements of target position with estimated covariance obtained by an unscented transformation (UT). The UT uses the mean and covariance from the camera measurements and from the UKF estimated aircraft states and feature locations to determine the estimated target mean and covariance. Simulation results confirm the concepts developed.