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
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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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