Xu, S., Ho, E.S.L. , Aslam, N. and Shum, H.P.H. (2018) Unsupervised abnormal behaviour detection with overhead crowd video. In: 2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Malabe, Sri Lanka, 6-8 December 2017, ISBN 9781538646021 (doi: 10.1109/SKIMA.2017.8294092)
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Abstract
Due to the increasing threat of terrorism, it has become more and more important to detect abnormal behaviour in public areas. In this paper, we introduce a system to identify pedestrians with abnormal movement trajectories in a scene using a data-driven approach. Our system includes two parts. The first part is an interactive tool that takes an overhead video as an input and tracks the pedestrians in a semi-automatic manner. The second part is a data-driven abnormal trajectories detection algorithm, which applies iterative k-means clustering to find out possible paths in the scene and thereby identifies those that do not fit well in any paths. Since the system requires only RGB video, it is compatible with most of the closed-circuit television (CCTV) systems used for security monitoring. Furthermore, the training of the abnormal trajectories detection algorithm is unsupervised and fully automatic. It means that the system can be deployed into a new location without manual parameter tuning and training data annotations. The system can be applied in indoor and outdoor environments and is best for automatic security monitoring.
| Item Type: | Conference Proceedings |
|---|---|
| Additional Information: | This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) (Ref: EP/M002632/1), the Royal Society (Ref: IE160609), and the Jiangsu Overseas Research & Training Program for University Prominent Young & Middle-aged Teachers and Presidents. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Ho, Dr Edmond S. L |
| Authors: | Xu, S., Ho, E.S.L., Aslam, N., and Shum, H.P.H. |
| College/School: | College of Science and Engineering > School of Computing Science |
| ISSN: | 2573-3214 |
| ISBN: | 9781538646021 |
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