On the Use of Distribution-based Metrics for the Evaluation of Drivers’ Fixation Maps Against Spatial Baselines

Abstract

A distinctive characteristic of human driver behavior is the spatial bias of gaze allocation toward the vanishing point of the road. This behavior can be evaluated by comparing fixation maps against a spatial-bias baseline using typical metrics such as the Pearson’s Correlation Coefficient (CC) and the Kullback-Leibler divergence (KL). CC and KL penalize false positives and negatives differently, which implies that they can be affected by the characteristics of the baseline. In this paper, we analyze the use of CC and KL for the evaluation of drivers’ fixation maps against two types of spatial-bias baselines: baselines obtained from recorded fixation maps (data-based) and 2D-Gaussian baselines (function-based). Our results indicate that the use of CC can lead to misleading interpretations due to single fixations outside of the spatial bias area when compared to data-based baselines. Thus, we argue that KL and CC should be considered simultaneously under specific modeling assumptions.