The core objective of this research is to develop an estimator capable of tracking the states of ground targets with observation measurements obtained from a single monocular camera mounted on a small unmanned aerial vehicle (UAV). Typical sensors on a small UAV include an inertial measurement unit (IMU) with three axes accelerometer and rate gyro sensors and a global positioning system (GPS) receiver which gives position and velocity estimates of the UAV. Camera images are combined with these measurements in state estimate filters to track ground features of opportunity and a target. The images are processed by a keypoint detection and matching algorithm that returns pixel coordinates for the features. Kinematic state equations are derived that reflect the relationships between the available input and output measurements and the states of the UAV, features, and target. These equations are used in the development of coupled state estimators for the dynamic state of the UAV, for estimation of feature positions, and for estimation of target position and velocity.

This paper presents improvements made to an automated machine vision system that identifies and inventories road signs. The system processes imagery from the Kansas Department of Transportation's road profiler that captures images every 26.4 feet on highways through out the state. The initial system processed images using a computationally efficient K-Means clustering algorithm twice, first on the original image and then again on a difference image to segment the images into objects. Next, object segments were classified based on their size and color. An additional classification step was applied examining the frame to frame trajectory that objects take through the field of view. This technique represented a crude form of triangulation. It was quite effective for signs along straight highways, but often failed along curves when trajectories deviate from the norm. This paper describes how full triangulation was implemented with Bundle adjustment to determine the object's physical location relative to the road profiler. Object locations are then added to the list of criteria determining classification. As with the original size and color classifiers, a representative image set was segmented and manually labeled to determine a joint probabilistic model characterizing the expected location of signs. Receiver Operating Characteristic curves were analyzed to adjust the thresholds for class identification. The improved sign inventory system was tested and its performance characteristics are presented.

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