Post-mission processing of video data in support of aircraft flight test poses an ever increasing challenge due to the enormous volume of digital data. The enormous data is a resultant of multiple sensors, increasing frame size and video rates. The analysis of video data requires an engineer to manually examine each frame in order to identify key events in each frame. In order to reduce the analysis time and labor, there is an inherent need to develop an automated processing capability in order to detect/identify/track objects from the video at high frame rates. The capability to automatically process high-frame rate video currently does not exist. In order to meet these challenges, PERL Research proposes a user-friendly software environment to address the Air Force's requirements. The key component of this software environment is an advanced learning algorithm based on statistical learning theory. Our integrated software approach includes the following capabilities: preprocessor for reducing the size of the original image; the ability to detect key events (based on previously trained data); learn new events on-line; track objects of interest; (5) and provide an intelligent output all the events detected/tracked
