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Exploring Discriminative Features For Anomaly Detection In Public Spaces, Shriguru Nayak, Archan Misra, Kasthuri Jeyarajah, Philips Kokoh Prasetyo, Ee-Peng Lim Apr 2015

Exploring Discriminative Features For Anomaly Detection In Public Spaces, Shriguru Nayak, Archan Misra, Kasthuri Jeyarajah, Philips Kokoh Prasetyo, Ee-Peng Lim

Research Collection School Of Information Systems

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 features (both spatial and temporal - e.g., occupancy volumes, rate of change in topic{specific tweets ...