Active tracking of surface targets in fused video

We seek to establish a quality metric for sensor fused video based on the performance of a multisensor system that actively tracks multiple surface targets over an extended field of regard Such a system must, in real-time, fuse multisensor/spectral imagery, detect targets reliably, track the targets for extended time periods as they move, stop, approach, cross, hide/emerge, over an extended field of regard. The system must automatically control the pointing, zoom and modes of cameras so as to maintain and re-establish track as targets move outside the current field of view. All this must be done for an increasing number and density of targets in clutter. Individual performance metrics relating to these various stages of processing include percent detection vs. false alarm rate, track identity lifetime, track purity, track accuracy, field of regard, target revisit rate, and number of targets tracked. All of these metrics reflect the quality of the multisensor imagery and the algorithms (image fusion, detection, tracking, sensor resource management) for the multi- target tracking task. This same task applies to human observers tracking multiple surface targets in displays of multisensor/spectral video. One intuitively expects that the "better" the image quality, the more "effectively" one can track multiple targets. We are exploring this relationship between tracking and fused video as a quality metric. This paper introduces the computational fused video tracking system that forms the basis of this new effort. Future papers will provide results and performance metrics noted above.