Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

4-2015

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

Mobile devices, Multilayers, Sensor fusion, Software, Web 2.0 technologies, Event Detection, Anomaly Detection, Urban Situation Awareness, Indoor Mobility, Twitter Analytics

Discipline

Databases and Information Systems | Digital Communications and Networking | Software Engineering

Research Areas

Data Science and Engineering; Software and Cyber-Physical Systems

Publication

Proceedings of SPIE: Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VI: 20-22April 2015, Baltimore

Volume

9464

First Page

1

Last Page

10

ISBN

9781628415803

Identifier

10.1117/12.2184316

Publisher

SPIE

City or Country

Bellingham, WA

Copyright Owner and License

Authors/LARC

Additional URL

https://doi.org/10.1117/12.2184316

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