Robust Autonomous Visual Detection and Tracking of Moving Targets in UAV Imagery

The use of Unmanned Aerial Vehicles (UAVs) for reconnaissance and surveillance applications has been steadily growing over the past few years. The operations of such largely autonomous systems rely primarily on the automatic detection and tracking of targets of interest. This paper presents a novel automatic multiple moving target detection and tracking framework that executes in real-time and is suitable for UAV imagery. The framework is based on image feature processing and projective geometry and is carried out on the following stages. First, outlier image features are computed with least median square estimation. Moving targets are subsequently detected by using a spatial clustering algorithm. Detected targets are tracked by using Kalman filtering while persistency check is used to discriminate between true moving targets and false detections. The proposed framework doesn't involve the explicit application of image transformations to detect potential targets resulting in enhanced computational time and reduction of registration errors. Furthermore, the use of data association to correlate detected and tracked targets along with the selective template update that's based on the data association decision significantly improves the overall tracking precision.