Observer analysis and its impact on task performance modeling

摘要

Fire fighters use relatively low cost thermal imaging cameras to locate hot spots and fire hazards in buildings. This research describes the analyses performed to study the impact of thermal image quality on fire fighter fire hazard detection task performance. Using human perception data collected by the National Institute of Standards and Technology (NIST) for fire fighters detecting hazards in a thermal image, an observer analysis was performed to quantify the sensitivity and bias of each observer. Using this analysis, the subjects were divided into three groups representing three different levels of performance. The top-performing group was used for the remainder of the modeling. Models were developed which related image quality factors such as contrast, brightness, spatial resolution, and noise to task performance probabilities. The models were fitted to the human perception data using logistic regression, as well as probit regression. Probit regression was found to yield superior fits and showed that models with not only 2nd order parameter interactions, but also 3rd order parameter interactions performed the best.