Unsupervised Abnormal Behaviour Detection with Overhead Crowd Video

Xu Shoujiang, Edmond Ho, Nauman Aslam, Hubert P. H. Shum

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2017
Subtitle of host publicationMalabe, Sri Lanka, 6-8 December 2017
Place of PublicationPiscataway, NJ
PublisherIEEE
ISBN (Electronic)9781538646021
ISBN (Print)9781538646038
DOIs
Publication statusPublished - Dec 2017
Event11th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2017 - Malabe, Sri Lanka
Duration: 6 Dec 20178 Dec 2017

Publication series

NameInternational Conference on Software, Knowledge Information, Industrial Management and Applications, SKIMA
Volume2017-December
ISSN (Print)2373-082X
ISSN (Electronic)2573-3214

Conference

Conference11th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2017
CountrySri Lanka
CityMalabe
Period6/12/178/12/17

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