Abstract

Military sensor applications include tasks such as the surveillance of activity and searching for roadside explosives. These tasks involve identifying and tracking specific objects in a cluttered scene. Unfortunately, the probability of accomplishing these tasks is not predicted by the traditional detect, recognize, and identify (DRI) target acquisition models. The reason why many security and surveillance tasks are functionally different from the traditional DRI tasks is described. Experiments using characters and simple shapes illustrate the problem with using the DRI model to predict the probability of identifying individual objects. The current DRI model is extended to predict specific object identification by including the frequency spectrum content of target contrast. The predictions of the new model match experimental data.

© 2007 Optical Society of America

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