Motion Based Video ATR

We propose a model-based temporal integration approach for automatic target recognition (ATR) using a single video stream. The video ATR problem is addressed as a verification and identification task. A generalized Hausdorff metric will be used for targetto model matching and target to target tracking and verification. The Hausdorff metric is more robust to noise and allows a confidence interpretation for target recognition/verification. Our system can address stationary and moving target ATR usingvideos acquired from stationary as well as moving platforms. When fully developed, the system will integrate camera motion compensation, target detection, target tracking, target pose estimation, target verification, and target fingerprinting components.In Phase I, we will develop robust algorithms for 3D target pose estimation and sequential target recognition using a generalized Hausdorff metric. We will test and refine our existing algorithms for sensor motion estimation, target detection andtracking, and best view selection for video ATR applications. We will develop techniques for reliable and descriptive vehicle fingerprint extraction. The algorithms will be evaluated using videos acquired using a turn table as well as surveillance videos.During Phase II, we will develop, demonstrate and evaluate a prototype system capable of robust video ATR. The primary military applications for the proposed work are in target recognition from unmanned moving platforms. Commercial applications are inface recognition from a single video, and tracking a cargo laden truck or a ship over a long period of time.