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Anomalous behavior detection in crowded scenes using clustering and spatio-temporal features

Version 2 2024-06-04, 06:14
Version 1 2016-12-08, 09:16
conference contribution
posted on 2024-06-04, 06:14 authored by M Yang, Sutharshan RajasegararSutharshan Rajasegarar, AS Rao, C Leckie, M Palaniswami
Anomalous behavior detection in crowded and unanticipated scenarios is an important problem in real-life applications. Detection of anomalous behaviors such as people standing statically and loitering around a place are the focus of this paper. In order to detect anomalous events and objects, ViBe was used for background modeling and object detection at first. Then, a Kalman filter and Hungarian cost algorithm were implemented for tracking and generating trajectories of people. Next, spatio-temporal features were extracted and represented. Finally, hyperspherical clustering was used for anomaly detection in an unsupervised manner. We investigate three different approaches to extracting and representing spatio-temporal features, and we demonstrate the effectiveness of our proposed feature representation on a standard benchmark dataset and a real-life video surveillance environment.

History

Volume

486

Pagination

132-141

Location

Melbourne, Victoria

Start date

2016-11-18

End date

2016-11-21

ISSN

1868-4238

ISBN-13

9783319483894

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2016, IFIP International Federation for Information Processing

Editor/Contributor(s)

Shi Z, Vadera S, Li G

Title of proceedings

IFIP 2016 : Proceedings of the 9th Intelligent Information Processing International Conference

Event

Intelligence Information Processing. International Conference (9th : 2016 : Melbourne, Victoria)

Publisher

Springer International

Place of publication

Cham, Switzerland

Series

IFIP Advances in Information and Communication Technology