Signboard Saliency Detection in Street Videos

Onkar Krishna, Kiyoharu Aizawa, Saskia Reimerth

Abstract: During the last few decades researchers in computer vision have proposed various saliency models for images with the common goal of classifying the image content by using the measure of importance. However, compared to still images, there is only a limited number of saliency detection algorithms proposed for video signals. However, predicting where a person looks in a video is relevant for applications such as advertisement design, video re-targeting and editing. In this work, we propose a novel method for video saliency detection that aims to detect the relative ranking of saliencies of signboards in street videos. For that reason, we collected eye-gaze data of participants viewing various street videos in free viewing and task viewing scenarios, where the task was to identify a place to have lunch at. Further, we quantitatively analyzed the collected eye-gaze data in order to generate the relative ranking of the signboards in the free viewing and the task viewing scenario. Based on the analysis' results, we propose a video saliency detection algorithm which can more accurately predict the relative saliencies of signboards in street videos. It can be seen that the prediction accuracy of our proposed model outperforms the existing video saliency detection algorithms.