Identifying High Velocity Objects in Complex Natural Environments Using Neural Networks

Real-time tracking of highly dynamic and complex objects has been an important aspect of testing in the aerospace industry for several decades. This paper proposes a methodology to identify complex, high velocity targets of interest in a video stream. The targets used in this case are aircrew escape systems, ejection seats, as they are propelled from a dynamic aircraft test bed. Additionally, the testing environment is cluttered with natural and man-made artifacts that affect the background appearance and make the video stream data visually noisy. The object identification problem involves the extraction of critical information from the video stream and feeding it into a neural network that determines the position of the target of interest within the field of view. The results of the computer simulation and analysis were mixed, and show that the proposed methodology is a potentially viable solution but will require continued development.