Comparison of relative effectiveness of video with serial visual presentation for target reconnaissance from UASs

Frank E. Skirlo; Anthony J. Matthews; Melvin Friedman; Brian L. Mark

doi:10.1117/12.2224796

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3 May 2016 Comparison of relative effectiveness of video with serial visual presentation for target reconnaissance from UASs

Frank E. Skirlo, Anthony J. Matthews, Melvin Friedman, Brian L. Mark

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Proceedings Volume 9820, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVII; 982008 (2016) https://doi.org/10.1117/12.2224796
Event: SPIE Defense + Security, 2016, Baltimore, Maryland, United States

Abstract

Reconnaissance from an unmanned aerial systems (UAS) is often done using video presentation. An alternate method is Serial Visual Presentation (SVP). In SVP, a static image remains in view until replaced by a new image at a rate equivalent to the live video. Mardell et al. have shown, in a forested environment, that a higher fraction of targets (people lost in the forest), are found with SVP than with video presentation. Here Mardell’s experiment is repeated for military targets in forested terrain at a fixed altitude. We too find a higher fraction of targets are found using SVP rather than video presentation. Typically it takes five seconds to cover a video field of view and at 30 frames per second. This implies that, for scenes where the target is not moving, 150 video images have nearly identical information (from a reconnaissance point of view) as a single SVP image. This is highly significant since transmission bandwidth is a limiting factor for most UASs. Finding targets in video or in SVP is an arduous task. For that reason we also compare aided target detection performance (Aided SVP) and unaided target detection performance on SVP images.

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Frank E. Skirlo, Anthony J. Matthews, Melvin Friedman, and Brian L. Mark "Comparison of relative effectiveness of video with serial visual presentation for target reconnaissance from UASs", Proc. SPIE 9820, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVII, 982008 (3 May 2016); https://doi.org/10.1117/12.2224796

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