In Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimise content creation, to develop user-centric services, or even to detect disorders in medical applications. Clustering users based on their common navigation patterns is a first direction to understand users behavior. However, classical clustering techniques fail in identifying this common paths, since they are usually focused on minimizing a simple distance metric. In this paper, we argue that minimizing the distance metric does not necessarily guarantee to identify users that experience similar navigation path in the VR domain. Therefore, we propose a graph-based method to identify clusters of users who are attending the same portion of the spherical content over time. The proposed solution takes into account the spherical geometry of the content and aims at clustering users based on the actual overlap of displayed content among users. Our method is tested on real VR user navigation patterns. Results show that our solution leads to clusters in which at least 85% of the content displayed by one user is shared among the other users belonging to the same cluster.
In this paper, we propose a novel graph-based clustering strategy able to detect meaningful clusters, i.e., group of users consuming the same portion of a virtual reality spherical content. First, we derived a geodesic distance threshold value to reflect the similarity among users, and then we built a clique-based clustering based on this metric. In graph theory, a set of nodes that are all connected to each other is called a clique. A clique perfectly matches with our definition of meaningful cluster: set of users all having signifi- cant pairwise viewport overlap, thus attending a common portion of video.
- Maximal cliques in the graph are detected by the BK algorithm.
- Among the resulting cliques, only the most populated one (with the highest cardinality) is kept as a cluster.
- A new affinity matrix is built, eliminating the entries corre- sponding to the elements of the cluster identified in Step 2.
These three steps are repeated until all nodes are assigned to clusters. It is worth mentioning that this iterative selection does not guaran- tee optimal clusters (i.e., maximal joint overlap within the cluster). However, i) it imposes viewport overlap among users within a clus- ter, ii) it identifies highly populated clusters, which can be translated in reliable trajectories/behaviours shared among users.
- Poster presented at ICASSP 2019 – International Conference on Acoustics, Speech, and Signal Processing in Brighton, United Kingdom.