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Visual Attention Models (VAMs) predict the location of an image or videoregions that are most likely to attract human attention. Although saliencydetection is well explored for 2D image and video content, there are only fewattempts made to design 3D saliency prediction models. Newly proposed 3D visualattention models have to be validated over large-scale video saliencyprediction datasets, which also contain results of eye-tracking information.There are several publicly available eye-tracking datasets for 2D image andvideo content. In the case of 3D, however, there is still a need forlarge-scale video saliency datasets for the research community for validatingdifferent 3D-VAMs. In this paper, we introduce a large-scale dataset containingeye-tracking data collected from 61 stereoscopic 3D videos (and also 2Dversions of those) and 24 subjects participated in a free-viewing test. Weevaluate the performance of the existing saliency detection methods over theproposed dataset. In addition, we created an online benchmark for validatingthe performance of the existing 2D and 3D visual attention models andfacilitate addition of new VAMs to the benchmark. Our benchmark currentlycontains 50 different VAMs.

Benchmark 3D eye-tracking dataset for visual saliency prediction on stereoscopic 3D video