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Profiling pedestrian distribution and anomaly detection in a dynamic environment

Doan, Minh Tuan, Rajasegarar, Sutharshan, Salehi, Mahsa, Moshtaghi, Masud and Leckie, Christopher 2015, Profiling pedestrian distribution and anomaly detection in a dynamic environment, in CIKM 2015: Proceedings of the Information and Knowledge Management 2015 International Conference, Association for Computing Machinery (ACM), New York, N.Y., pp. 1827-1830, doi: 10.1145/2806416.2806645.

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Title Profiling pedestrian distribution and anomaly detection in a dynamic environment
Author(s) Doan, Minh Tuan
Rajasegarar, Sutharshan
Salehi, Mahsa
Moshtaghi, Masud
Leckie, Christopher
Conference name Association for Computing Machinery Information and Knowledge Management. International Conference (24th : 2015 : Melbourne, Vic.)
Conference location Melbourne, Vic.
Conference dates 2015/10/19 - 2015/10/23
Title of proceedings CIKM 2015: Proceedings of the Information and Knowledge Management 2015 International Conference
Editor(s) [Unknown]
Publication date 2015
Conference series Association for Computing Machinery Information and Knowledge Management International Conference
Start page 1827
End page 1830
Total pages 4
Publisher Association for Computing Machinery (ACM)
Place of publication New York, N.Y.
Keyword(s) Anomaly detection
Application
Anomlay detection
Summary Pedestrians movements have a major impact on the dynamics of cities and provide valuable guidance to city planners. In this paper we model the normal behaviours of pedestrian flows and detect anomalous events from pedestrian counting data of the City of Melbourne. Since the data spans an extended period, and pedestrian activities can change intermittently (e.g., activities in winter vs. summer), we applied an Ensemble Switching Model, which is a dynamic anomaly detection technique that can accommodate systems that switch between different states. The results are compared with those produced by a static clustering model (Hy-CARCE) and also cross-validated with known events. We found that the results from the Ensemble Switching Model are valid and more accurate than HyCARCE.
ISBN 9781450337946
Language eng
DOI 10.1145/2806416.2806645
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890103 Mobile Data Networks and Services
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, Association for Computing Machinery (ACM)
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082146

Document type: Conference Paper
Collection: School of Information Technology
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