Impact of georegistration accuracy on wide area motion imagery object detection and tracking


Abstract

Advances in sensor technologies and embedded low power processing provide new opportunities for using Wide Area Motion Imagery (WAMI) across a spectrum of mapping and monitoring applications covering large geospatial areas for extended time periods. While significant developments have been made in video analytics for ground or low-altitude aerial videos, methods for WAMI have been limited due to lack of benchmarking datasets, data format complexities, lack of labeled training videos, and high data processing requirements. This paper aims to help advance the broader use of WAMI by evaluating the georegistration accuracy and its impact on downstream video analytics using two benchmark datasets (CLIF 2007, ABQ 2013). In addition to the current intensified interest in using deep learning for aerial object recognition and tracking, this paper motivates the need for further development of more robust and fast georegistration algorithms for multi-camera WAMI systems.