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Augmented reality (AR) overlays digital content onto the reality. In ARsystem, correct and precise estimations of user's visual fixations and headmovements can enhance the quality of experience by allocating more computationresources on the areas of interest. However, there is inadequate research aboutunderstanding the visual exploration of users when using an AR system ormodeling AR visual attention. To bridge the gap between the saliency predictionon real-world scene and on scene augmented by virtual information, we constructthe ARVR saliency dataset with 12 diverse videos viewed by 20 people. Thevirtual reality (VR) technique is employed to simulate the real-world.Annotations of object recognition and tracking as augmented contents areblended into the omnidirectional videos. The saliency annotations of head andeye movements for both original and augmented videos are collected and togetherconstitute the ARVR dataset. We also design a model which is capable of solvingthe saliency prediction problem in AR. Local block images are extracted tosimulate the viewport and offset the projection distortion. Conspicuous visualcues in local viewports are extracted to constitute the spatial features. Theoptical flow information is estimated as the important temporal feature. Wealso consider the interplay between virtual information and reality. Thecomposition of the augmentation information is distinguished, and the jointeffects of adversarial augmentation and complementary augmentation areestimated. We generate a graph by taking each block image as one node. Both thevisual saliency mechanism and the characteristics of viewing behaviors areconsidered in the computation of edge weights on the graph which areinterpreted as Markov chains. The fraction of the visual attention that isdiverted to each block image is estimated through equilibrium distribution onof this chain.

Toward Better Understanding of Saliency Prediction in Augmented 360 Degree Videos