Unsupervised Abnormal Behaviour Detection with Overhead Crowd Video

Xu, Shoujiang, Ho, Edmond S. L., Aslam, Nauman and Shum, Hubert P. H. (2017) Unsupervised Abnormal Behaviour Detection with Overhead Crowd Video. In: Proceedings of the 11th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2017: Malabe, Sri Lanka, 6-8 December 2017. International Conference on Software, Knowledge Information, Industrial Management and Applications, SKIMA (2017). IEEE, Piscataway, NJ, p. 8294092. ISBN 9781538646038, 9781538646021

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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:	Book Section
Uncontrolled Keywords:	Trajectory, Security, Databases, Feature extraction, Monitoring, Clustering algorithms, Mathematical model
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:	Organisation
Depositing User:	Hubert Shum
Date Deposited:	03 Nov 2017 11:11
Last Modified:	14 Aug 2020 09:12
URI:	http://nrl.northumbria.ac.uk/id/eprint/32442

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