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The spherical domain representation of 360 video/image presents manychallenges related to the storage, processing, transmission and rendering ofomnidirectional videos (ODV). Models of human visual attention can be used sothat only a single viewport is rendered at a time, which is important whendeveloping systems that allow users to explore ODV with head mounted displays(HMD). Accordingly, researchers have proposed various saliency models for 360video/images. This paper proposes ATSal, a novel attention based (head-eye)saliency model for 360\degree videos. The attention mechanism explicitlyencodes global static visual attention allowing expert models to focus onlearning the saliency on local patches throughout consecutive frames. Wecompare the proposed approach to other state-of-the-art saliency models on twodatasets: Salient360! and VR-EyeTracking. Experimental results on over 80 ODVvideos (75K+ frames) show that the proposed method outperforms the existingstate-of-the-art.

ATSal: An Attention Based Architecture for Saliency Prediction in 360 Videos