|dc.contributor.author||Yang, Ji Hyun||
|dc.identifier.citation||Aviation, Space, and Environmental Medicine, Vol. 83, No. 6, June 2012||
|dc.description||The article of record as published may be located at http://dx.doi.org/10.3357/ASEM.3230.2012||en_US
|dc.description.abstract||Introduction: 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
|dc.rights||This 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.title||Modeling Peripheral Vision for Moving Target Search and Detection||en_US
|dc.contributor.department||Modeling, Virtual Environments, and Simulation Institute (MOVES)||
|dc.subject.author||search and target acquisition||en_US