A computer graphics based target detection model

Modeling of visual perception for computer-generated forces and intelligent software agents is usually fairly feeble in computer games and military simulations. Most of the time, tricks or shortcuts are employed in the perceptual model. Under certain conditions, these shortcuts cause unrealistic beh...

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Bibliographic Details
Main Author: Jones, Brian Edward.
Other Authors: Darken, Christian J.
Format: Others
Published: Monterey, California. Naval Postgraduate School 2012
Subjects:
Online Access:http://hdl.handle.net/10945/2628
Description
Summary:Modeling of visual perception for computer-generated forces and intelligent software agents is usually fairly feeble in computer games and military simulations. Most of the time, tricks or shortcuts are employed in the perceptual model. Under certain conditions, these shortcuts cause unrealistic behavior and detract from military training and user immersion into the simulated environment. Many computer games and simulations trace a ray between the target and observer to determine if the observer can see the target. More complex models are sometime used in military simulations. One of these models used in Army simulations is the ACQUIRE model. This model still may produce debatable results. The ACQUIRE visual perception model uses a single value for the targetâ s contrast with its background. This can cause unrealistic results in certain conditions, allowing computer-generated forces to see targets that should not be seen and not see targets that should. Testing these more complex models needs to be completed to determine the conditions under which the model gives questionable results. Testing ACQUIRE against human subjects helped determine when ACQUIRE behaves reasonably. The study consisted of multiple scenes with a target in many positions, multiple postures, and many different lighting and fog conditions. Now that testing and analysis is complete, modifications can be made to the visual perception model allowing it to give better results in more varied conditions, such as: low light, excessive fog conditions, and partially hidden targets.