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dc.contributor.authorYang, Ji Hyun
dc.contributor.authorHuston, Jesse
dc.contributor.authorDay, Michael
dc.contributor.authorBalogh, Imre
dc.date.accessioned2014-05-29T15:21:40Z
dc.date.available2014-05-29T15:21:40Z
dc.date.issued2012-06
dc.identifier.citationAviation, Space, and Environmental Medicine, Vol. 83, No. 6, June 2012
dc.identifier.urihttp://hdl.handle.net/10945/41686
dc.descriptionThe article of record as published may be located at http://dx.doi.org/10.3357/ASEM.3230.2012en_US
dc.description.abstractIntroduction: Most target search and detection models focus on foveal vision. In reality, peripheral vision plays a signifi cant role, especially in detecting moving objects. Methods: There were 23 subjects who participated in experiments simulating target detection tasks in urban and rural environments while their gaze parameters were tracked. Button responses associated with foveal object and peripheral object (PO) detection and recognition were recorded. In an urban scenario, pedestrians appearing in the periphery holding guns were threats and pedestrians with empty hands were non-threats. In a rural scenario, non- U.S. unmanned aerial vehicles (UAVs) were considered threats and U.S. UAVs non-threats. Results: On average, subjects missed detecting 2.48 POs among 50 POs in the urban scenario and 5.39 POs in the rural scenario. Both saccade reaction time and button reaction time can be predicted by peripheral angle and entrance speed of POs. Fast moving objects were detected faster than slower objects and POs appearing at wider angles took longer to detect than those closer to the gaze center. A second-order mixed-effect model was applied to provide each subject’s prediction model for peripheral target detection performance as a function of eccentricity angle and speed. About half the subjects used active search patterns while the other half used passive search patterns. Discussion: An interactive 3-D visualization tool was developed to provide a representation of macro-scale head and gaze movement in the search and target detection task. An experimentally validated stochastic model of peripheral vision in realistic target detection scenarios was developed.en_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleModeling Peripheral Vision for Moving Target Search and Detectionen_US
dc.typeArticleen_US
dc.contributor.departmentModeling, Virtual Environments, and Simulation Institute (MOVES)
dc.subject.authorperipheral visionen_US
dc.subject.authortarget detectionen_US
dc.subject.authorrecognitionen_US
dc.subject.authorsearch and target acquisitionen_US


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