Violent behaviour detection using local trajectory response
Violent behaviour detection using local trajectory response
- Author(s): K. Lloyd ; P.L. Rosin ; A.D. Marshall ; S.C. Moore
- DOI: 10.1049/ic.2016.0082
For access to this article, please select a purchase option:
Buy conference paper PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016) — Recommend this title to your library
Thank you
Your recommendation has been sent to your librarian.
- Author(s): K. Lloyd ; P.L. Rosin ; A.D. Marshall ; S.C. Moore Source: 7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016), 2016 page ()
- Conference: 7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016)
- DOI: 10.1049/ic.2016.0082
- ISBN: 978-1-78561-400-2
- Location: Madrid, Spain
- Conference date: 23-25 Nov. 2016
- Format: PDF
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.
Inspec keywords: image classification; computer vision; video surveillance; image motion analysis; behavioural sciences; police data processing
Subjects: Computer vision and image processing techniques; Image recognition; Video signal processing; Social and behavioural sciences computing
Related content
content/conferences/10.1049/ic.2016.0082
pub_keyword,iet_inspecKeyword,pub_concept
6
6