Billboard Saliency Detection in Street Videos for Adults and Elderly

Onkar Krishna, Kiyoharu Aizawa

Abstract: Detecting salient locations in images and videos that attract human attention is a challenging problem in computer vision. Most of the models developed for this purpose are either based on the bottom-up features of a scene or top-down factors. Observer's age is one of the important top-down factor which influences viewing behavior, some recent studies proposed age-adapted models to predict saliencies for different age groups. However, these studies reported age-impact on eye-gaze distribution for images only and videos still remain unexplored. In this study, we analyze the differences in eye gaze landings between adults and elderly while viewing street videos and develop a novel application of detecting saliency of billboards in the paved area of the street for adult and elderly. Our proposed age-adapted saliency model outperforms the existing video saliency models towards predicting the billboard saliency.