Wide Area Motion Imagery (WAMI) systems used on surveillance aircraft may suffer from system calibration errors associated with frequent re-installation. These geo-coding errors corrupt the quality of mapping of the tracked objects, ’movers’, from the image frame into world reference frame. In this study, an automated system for calibration of the imagery captured with six-camera WAMI array has been developed. The automatic calibration was achieved by a system of several multi-scale feature classifiers adaptively applied to an image captured by the camera array dependent on the feature availability and classifier accuracy. The feature extraction and association modules were designed to be operating interchangeably on a frame from any given camera. The choice of the module was performed automatically using a decision tree designed as a part of the system architecture. Calculation of the per-frame corrections to mitigate the localisation error of the movers was performed by associating the features detected in each individual camera and features extracted from available satellite imagery used as a datum. The effects of the distance to the feature and the choice of the feature extraction module on the mover localisation accuracy have been evaluated on 300 frames (6 images each) captured with the WAMI array. Significant reduction in the magnitude of the geo-coding error (from 15.77-36.54 m to 5.42-8.55 m on average) was achieved and can be seen in improved alignment of the features projected into the frame as well as the reliable mapping of the mover trajectories across frames. Unlike similar systems, focusing on post-processing, the WAMI calibration system presented in the paper was designed for continuous parameter estimation in real-time
Anastasiia Volkova and Peter W. Gibbens, "Automated WAMI system calibration procedure based on multi-scale fusion and adaptive data association for geo-coding error correction," Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 106490H (Presented at SPIE Defense + Security: April 18, 2018; Published: 30 April 2018); https://doi.org/10.1117/12.2304680.
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