Benchmark three-dimensional eye-tracking dataset for visual saliency prediction on stereoscopic three-dimensional video

Abstract

Visual attention models (VAMs) predict the location of image or video regions that are most likely to attract human attention. Although saliency detection is well explored for two-dimensional (2-D) image and video content, there have been only a few attempts made to design three-dimensional (3-D) saliency prediction models. Newly proposed 3-D VAMs have to be validated over large-scale video saliency prediction datasets, which also contain results of eye-tracking information. There are several publicly available eye-tracking datasets for 2-D image and video content. In the case of 3-D, however, there is still a need for large-scale video saliency datasets for the research community for validating different 3-D VAMs. We introduce a large-scale dataset containing eye-tracking data collected from 61 stereoscopic 3-D videos (and also 2-D versions of those), and 24 subjects participated in a free-viewing test. We evaluate the performance of the existing saliency detection methods over the proposed dataset. In addition, we created an online benchmark for validating the performance of the existing 2-D and 3-D VAMs and facilitating the addition of new VAMs to the benchmark. Our benchmark currently contains 50 different VAMs.