Detectability prediction in dynamic scenes for enhanced environment perception

A driver assistance system realizes that the driver is distracted and that a potentially hazardous situation is emerging. Where should it guide the attention of the driver? Optimally to the spot that allows the driver to make the best decision. Pedestrian detectability has been proposed recently as a measure of the probability that a driver perceives pedestrians in an image [9]. Leveraging this information allows a driver assistance system to direct the attention of the driver to the spot that maximizes the probability that all pedestrians are seen. In this paper we extend this concept to dynamic scenes. We use an annotated video dataset recorded from a moving car in an urban environment and acquire the detectabilities of pedestrians via a psychophysical experiment. Based on these measured detectabilites we train a machine learning algorithm to predict detectabilities from a set of image features. We then exploit this mapping to predict the optimal focus of attention in a second experiment, thus demonstrating the usefulness of our method in a dynamic driver assistance context.