@inproceedings{76a227ebc4d44e83bd6076373cac7347,
title = "Exploring discriminative features for anomaly detection in public spaces",
abstract = "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 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 and activities of crowds in public spaces.",
keywords = "Anomaly Detection, Event Detection, Indoor Mobility, Twitter Ana-lytics, Urban Situation Awareness",
author = "Shriguru Nayak and Archan Misra and Kasthuri Jayarajah and Prasetyo, {Philips Kokoh} and Lim, {Ee Peng}",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VI ; Conference date: 20-04-2015 Through 22-04-2015",
year = "2015",
doi = "10.1117/12.2184316",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Kolodny, {Michael A.} and Tien Pham",
booktitle = "Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VI",
address = "United States",
}