Using the Support Vector Regression Approach to Model Human Performance

TLDR: This paper applies support vector regression (SVR) to a real-world human factors problem of night vision system design for passenger vehicles by modeling the probability of pedestrian detection as a function of image metrics and results indicate that the SVR-based model of pedestrians detection shows good performance.

Abstract: Empirical data modeling can be used to model human performance and explore the relationships between diverse sets of variables. A major challenge of empirical data modeling is how to generalize or extrapolate the findings with a limited amount of observed data to a broader context. In this paper, we introduce an approach from machine learning, known as support vector regression (SVR), which can help address this challenge. To demonstrate the method and the value of modeling human performance with SVR, we apply SVR to a real-world human factors problem of night vision system design for passenger vehicles by modeling the probability of pedestrian detection as a function of image metrics. The results indicate that the SVR-based model of pedestrian detection shows good performance. Some suggestions on modeling human performance by using SVR are discussed.