Understanding User Navigation in Immersive Experience: an Information-Theoretic analysis
Overview of user behaviour analysis in a VR system: A) Collection of user’s trajectories during immersive experiments. B) The raw data collected from different users and content are stored in a database. C) After a general pre-processing (i.e., re-sampling), the VR trajectories are transformed in the most suitable format for the final analysis. D) Information-theory metrics are applied to the VR trajectories looking for the desired characteristics: intra- and inter-user behaviour analysis.
Abstract
To cope with the large bandwidth and low-latency requirements, Virtual Reality (VR) systems are steering toward user-centric systems in which coding, streaming, and possibly rendering are personalized to the final user. The success of these user-centric VR systems mainly relies on the ability to anticipate viewers' navigation. This has motivated large attention to studying the prediction of user’s movements in a VR experience. However, most of these works lack a proper and exhaustive behavioural analysis in a VR scenario, leaving many key-behavioural questions unsolved and unexplored: Can some users be more predictable than others? Do users have their own way of navigating and how much is this affected by the video content features? Can we quantify the similarity of users' navigation? Answering these questions is a crucial step toward the understanding of user behaviour in VR; it is the overall goal of this paper. By studying VR trajectories across different contents and through information-theoretic tools, we aim to characterise navigation patterns both for every single viewer (profiling individually viewers – intra-user analysis) and for a multitude of viewers (identifying common patterns among viewers – inter-user analysis). For each of these proposed behavioural analyses, we describe the applied metrics and key observations that can be extrapolated.
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