Exploring Discriminative Features For Anomaly Detection In Public Spaces

Context data,, collected either from mobile devices or from user generated social media, content, can help identify abnormal behavioural patterns in public spaces (e.g., shopping malls, college campuses or downtown city areas). Spatiotemporal analysis of such data, streams provides a compelling; new approach towards automatically creating real-time urban situational awareness, especially about events that are unanticipated or that evolve very rapidly. In this work, we use real life datasets collected via SMU's LiveLabs testbed or via SMU's Palanteer software, to explore various discriminative ye features (both spatial and temporal - e.g., occupancy volumes, rate of change in topic specific tweets or probabilistic distribution of group sizes) for such anomaly detection. We show that such feature primitives fit, into a future multi-layer sensor fusion framework that, can provide valuable insights into mood & activities of crowds in public spaces.