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Profiling pedestrian distribution and anomaly detection in a dynamic environment
conference contribution
posted on 2015-01-01, 00:00 authored by M T Doan, Sutharshan RajasegararSutharshan Rajasegarar, M Salehi, M Moshtaghi, C LeckiePedestrians 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.
History
Event
Association for Computing Machinery Information and Knowledge Management. International Conference (24th : 2015 : Melbourne, Vic.)Volume
19-23-Oct-2015Pagination
1827 - 1830Publisher
Association for Computing Machinery (ACM)Location
Melbourne, Vic.Place of publication
New York, N.Y.Publisher DOI
Start date
2015-10-19End date
2015-10-23ISBN-13
9781450337946Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2015, Association for Computing Machinery (ACM)Editor/Contributor(s)
[Unknown]Title of proceedings
CIKM 2015: Proceedings of the Information and Knowledge Management 2015 International ConferenceUsage metrics
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