Improved Human Movement Tracking and Prediction in High-Occlusion Multi-user Virtual Reality Environments

Virtual reality has the ability to change the way people work and play together, but almost all VR technology to date is designed for single users in separate, dedicated spaces. The problem with having multiple people in the same shared room (while in a shared VR experience together) is that they become much more difficult for the sensors to accurately track because sensors become visually blocked more often. This research proposal is focused on using intelligent algorithms to help improve tracking accuracy in these situations, while keeping hardware costs as low as possible. Using this technology helps VRCAVE can share its multi-user VR experiences with as many people as possible in a cost-effective way.

Faculty Supervisor:

Pierre Boulanger


Nathaniel Rossol




Computer science


Information and communications technologies




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