  Augmented reality (AR) overlays digital content onto the reality. In AR
system, correct and precise estimations of user's visual fixations and head
movements can enhance the quality of experience by allocating more computation
resources on the areas of interest. However, there is inadequate research about
understanding the visual exploration of users when using an AR system or
modeling AR visual attention. To bridge the gap between the saliency prediction
on real-world scene and on scene augmented by virtual information, we construct
the ARVR saliency dataset with 12 diverse videos viewed by 20 people. The
virtual reality (VR) technique is employed to simulate the real-world.
Annotations of object recognition and tracking as augmented contents are
blended into the omnidirectional videos. The saliency annotations of head and
eye movements for both original and augmented videos are collected and together
constitute the ARVR dataset. We also design a model which is capable of solving
the saliency prediction problem in AR. Local block images are extracted to
simulate the viewport and offset the projection distortion. Conspicuous visual
cues in local viewports are extracted to constitute the spatial features. The
optical flow information is estimated as the important temporal feature. We
also consider the interplay between virtual information and reality. The
composition of the augmentation information is distinguished, and the joint
effects of adversarial augmentation and complementary augmentation are
estimated. We generate a graph by taking each block image as one node. Both the
visual saliency mechanism and the characteristics of viewing behaviors are
considered in the computation of edge weights on the graph which are
interpreted as Markov chains. The fraction of the visual attention that is
diverted to each block image is estimated through equilibrium distribution on
of this chain.

Image and Video Processing;Computer Vision and Pattern Recognition;Human-Computer Interaction