  Ego-centric data streams provide a unique opportunity to reason about joint
behavior by pooling data across individuals. This is especially evident in
urban environments teeming with human activities, but which suffer from
incomplete and noisy data. Collaborative human activities exhibit common
spatial, temporal, and visual characteristics facilitating inference across
individuals from multiple sensory modalities as we explore in this paper from
the perspective of meetings. We propose a new Bayesian nonparametric model that
enables us to efficiently pool video and GPS data towards collaborative
activities analysis from multiple individuals. We demonstrate the utility of
this model for inference tasks such as activity detection, classification, and
summarization. We further demonstrate how spatio-temporal structure embedded in
our model enables better understanding of partial and noisy observations such
as localization and face detections based on social interactions. We show
results on both synthetic experiments and a new dataset of egocentric video and
noisy GPS data from multiple individuals.
