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

• Published in: Journal of electronic imaging 2016-01, Vol.25 (1), p.013008-013008
• Main Authors: Banitalebi-Dehkordi, Amin, Nasiopoulos, Eleni, Pourazad, Mahsa T, Nasiopoulos, Panos
• Format: Article
• Language: English
• ISSN: 1017-9909; 1560-229X
• DOI: 10.1117/1.JEI.25.1.013008

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.