Violent behaviour detection using local trajectory response

Lloyd, Kaelon, Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884, Marshall, Andrew D. ORCID: https://orcid.org/0000-0003-2789-1395 and Moore, Simon C. ORCID: https://orcid.org/0000-0001-5495-4705 2017. Violent behaviour detection using local trajectory response. Presented at: 7th International Conference on Imaging for Crime Detection and Prevention, Madrid, Spain, 23-25 November 2016. 7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016). IET Seminar Digests 2016/0006 Institution of Engineering and Technology, pp. 78-83. 10.1049/ic.2016.0082

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Abstract

Surveillance systems in the United Kingdom are prominent, and the number of installed cameras is estimated to be around 1.8 million. It is common for a single person to watch multiple live video feeds when conducting active surveillance, and past research has shown that a person’s effectiveness at successfully identifying an event of interest diminishes the more monitors they must observe. We propose using computer vision techniques to produce a system that can accurately identify scenes of violent behaviour. In this paper we outline three measures of motion trajectory that when combined produce a response map that highlights regions within frames that contain behaviour typical of violence based on local information. Our proposed method demonstrates state-of-the-art classification ability when given the task of distinguishing between violent and non-violent behaviour across a wide variety of violent data, including real-world surveillance footage obtained from local police organisations.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Crime and Security Research Institute (CSURI) Dentistry
Publisher:	Institution of Engineering and Technology
ISBN:	978-1-5108-4524-4
Date of First Compliant Deposit:	1 September 2017
Date of Acceptance:	3 October 2016
Last Modified:	05 Jan 2024 02:05
URI:	https://orca.cardiff.ac.uk/id/eprint/104200

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