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Recognition of Anomalous Motion Patterns in Urban Surveillance
Swedish Defence Research Agency, Sweden.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
INO, Canada.
INO, Canada.
2013 (English)In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, Vol. 7, no 1, 102-110 p.Article in journal (Refereed) Published
Abstract [en]

We investigate the unsupervised K-means clustering and the semi-supervised hidden Markov model (HMM) to automatically detect anomalous motion patterns in groups of people (crowds). Anomalous motion patterns are typically people merging into a dense group, followed by disturbances or threatening situations within the group. The application of K-means clustering and HMM are illustrated with datasets from four surveillance scenarios. The results indicate that by investigating the group of people in a systematic way with different K values, analyze cluster density, cluster quality and changes in cluster shape we can automatically detect anomalous motion patterns. The results correspond well with the events in the datasets. The results also indicate that very accurate detections of the people in the dense group would not be necessary. The clustering and HMM results will be very much the same also with some increased uncertainty in the detections.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2013. Vol. 7, no 1, 102-110 p.
Keyword [en]
Clustering algorithms, Decision support systems, Hidden Markov models, Machine learning, Machine vision, Object segmentation, Pattern recognition
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-93983DOI: 10.1109/JSTSP.2013.2237882ISI: 000318435000010OAI: diva2:628236

Funding Agencies|Vinnova (Swedish Governmental Agency for Innovation Systems) under the VINNMER program||

Available from: 2013-06-13 Created: 2013-06-13 Last updated: 2013-07-23

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Gustafsson, Fredrik
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Automatic ControlThe Institute of Technology
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