Reliable motion detection of small targets in video with low signal-to-clutter ratios

Studies show that vigilance decreases rapidly after several minutes when human operators are required to search live video for infrequent intrusion detections. Therefore, there is a need for systems which can automatically detect targets in live video and reserve the operator's attention for assessment only. Thus far, automated systems have not simultaneously provided adequate detection sensitivity, false alarm suppression, and ease of setup when used in external, unconstrained environments. This unsatisfactory performance can be exacerbated by poor video imagery with low contrast, high noise, dynamic clutter, image misregistration, and/or the presence of small, slow, or erratically moving targets. This paper describes a highly adaptive video motion detection and tracking algorithm provides good performance under stressing data and environmental conditions. Features of the algorithm include: reliable detection with negligible false alarm rate of variable velocity targets having low signal-to- clutter ratios; reliable tracking of targets that exhibit motion that is non-inertial, i.e. varies in direction and velocity; automatic adaptation to both infrared and visible imagery with variable quality; and suppression of false alarms caused by sensor flaws and/or cutouts.