Pedestrian detectability: Predicting human perception performance with machine vision

Abstract

How likely is it that a driver notices a person standing on the side of the road? In this paper we introduce the concept of pedestrian detectability. It is a measure of how probable it is that a human observer perceives pedestrians in an image. We acquire a dataset of pedestrians with their associated detectabilities in a rapid detection experiment using images of street scenes. On this dataset we learn a regression function that allows us to predict human detectabilities from an optimized set of image and contextual features. We exploit this function to infer the optimal focus of attention for pedestrian detection. With this combination of human perception and machine vision we propose a method we deem useful for the optimization of Human-Machine-Interfaces in driver assistance systems.