  Detection of interacting and conversational groups from images has
applications in video surveillance and social robotics. In this paper we build
on prior attempts to find conversational groups by detection of social
gathering spaces called o-spaces used to assign people to groups. As our
contributions to the task, we are the first paper to incorporate features
extracted from the room layout image, and the first to incorporate a deep
network to generate an image representation of the proposed o-spaces.
Specifically, this novel network builds on the PointNet architecture which
allows unordered inputs of variable sizes. We present accuracies which
demonstrate the ability to rival and sometimes outperform the best models, but
due to a data imbalance issue we do not yet outperform existing models in our
test results.
