Computer Graphics-Based Models Of Target Detection: Algorithms,Comparison To Human Performance,And Failure Modes

Abstract

Target detection is one of the fundamental phenomena that must be modeled in military simulations. When the target detection model fails, entities that should not be mutually aware engage, and entities that should fight ignore one another. The potential negative consequences for training and analysis are obvious. We describe three closely related computer graphics-based detection models for virtual simulation that can avoid some of the limitations of previous approaches. These models incorporate a standard target detection model, but feed it with more accurate target exposure and contrast data than has been done previously. Two variants of the base model attempt to improve the target contrast calculation and add color sensitivity. We compare the predictions of these models to human performance to show that the model variants have their intended effect. The performance of even the best models can deviate drastically from the performance of the human eye under some circumstances represented in our experiment. We lump these into categories as an aid to understanding the state of the art and to motivate future research.