User Behaviour Analysis in Immersive Systems

One of the major challenges for the next decade is to design virtual and augmented reality systems (virtual reality systems at large) for real-world use cases such as healthcare, entertainment and, e-education. In these novel applications, the user becomes an active consumer that interacts with the content in a fully immersive environment. This interactivity however can push the limits of both connectivity and computation. To avoid this, VR interactive systems will need to operate at scale in a personalized manner, remaining bandwidth-tolerant whilst meeting quality and latency criteria. This can be accomplished only by a fundamental revolution of the coding/streaming/rendering chain that has to put the interactive user at the heart rather than at the end of the chain – as is the case of the classical non-interactive streaming systems. One major limitation is the lack of understanding of users’ behaviour in a VR experience. Thus, we are interested in quantifying similarities not only among different users but also for the same viewer across contents. We strongly believe these investigations can bring key information to the understanding of any hidden patterns of immersive user navigation.

In this work, we propose a graph-based method to identify clusters of users attending the same portion of the spherical content over time.

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This work advances the state-of-the-art in both the study of users’ behaviour in VR and the user-centric system design.

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In this work, we aim to characterise navigation patterns for both a single viewer (intra-user analysis) and a multitude of viewers (inter-user analysis).

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Describe the item and include any relevant details. Click to edit the text.
In this work, we propose a graph-based method to identify clusters of users attending the same portion of the spherical content over time.
This work advances the state-of-the-art in both the study of users’ behaviour in VR and the user-centric system design.
In this work, we aim to characterise navigation patterns for both a single viewer (intra-user analysis) and a multitude of viewers (inter-user analysis).