Exploring discriminative features for anomaly detection in public spaces

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 app...

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Bibliographic Details
Main Authors: NAYAK, Shriguru, MISRA, Archan, JEYARAJAH, Kasthuri, PRASETYO, Philips Kokoh, Ee-peng LIM
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2015
Subjects:
Mobile devices
Multilayers
Sensor fusion
Software
Web 2.0 technologies
Event Detection
Anomaly Detection
Urban Situation Awareness
Indoor Mobility
Twitter Analytics
Databases and Information Systems
Digital Communications and Networking
Software Engineering
Online Access:https://ink.library.smu.edu.sg/sis_research/3138
https://ink.library.smu.edu.sg/context/sis_research/article/4138/viewcontent/P_ID_52611_SPIE_2015_LocationBasedAnomaly.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/3138
https://ink.library.smu.edu.sg/context/sis_research/article/4138/viewcontent/P_ID_52611_SPIE_2015_LocationBasedAnomaly.pdf

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