Machine vision application to automatic intruder detection using CCTV

Hernando Fernandez-Canque; Sorin Hintea; John Freer; Ali Ahmadinia

doi:10.1007/978-3-642-04592-9_62

Machine vision application to automatic intruder detection using CCTV

Hernando Fernandez-Canque, Sorin Hintea, John Freer, Ali Ahmadinia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The work presented in this paper addresses the application of new technologies to the task of intruder monitoring. It presents an innovative Machine Vision application to detect and track a person in a Closed Circuit Television System (CCTV) identifying suspicious activity. Neural Network techniques are applied to identify suspicious activities from the trajectory path, speed, direction and risk areas for a person in a scene, as well as human posture. Results correlate well with operator determining suspicious activity. The automated system presented assists an operator to increase reliability and to monitor large numbers of surveillance cameras.

Original language	English
Title of host publication	Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems
Publisher	Springer-Verlag
ISBN (Print)	9783642045912
DOIs	https://doi.org/10.1007/978-3-642-04592-9_62
Publication status	Published - 1 Jan 2009

Keywords

CCTV
machine vision
automatic intruder detection
intruder monitoring

Documents and Links

10.1007/978-3-642-04592-9_62

http://researchonline.gcu.ac.uk/sec/304

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Fernandez-Canque, H., Hintea, S., Freer, J., & Ahmadinia, A. (2009). Machine vision application to automatic intruder detection using CCTV. In Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems Springer-Verlag. https://doi.org/10.1007/978-3-642-04592-9_62

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abstract = "The work presented in this paper addresses the application of new technologies to the task of intruder monitoring. It presents an innovative Machine Vision application to detect and track a person in a Closed Circuit Television System (CCTV) identifying suspicious activity. Neural Network techniques are applied to identify suspicious activities from the trajectory path, speed, direction and risk areas for a person in a scene, as well as human posture. Results correlate well with operator determining suspicious activity. The automated system presented assists an operator to increase reliability and to monitor large numbers of surveillance cameras.",

keywords = "CCTV, machine vision, automatic intruder detection, intruder monitoring",

author = "Hernando Fernandez-Canque and Sorin Hintea and John Freer and Ali Ahmadinia",

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Machine vision application to automatic intruder detection using CCTV. / Fernandez-Canque, Hernando; Hintea, Sorin; Freer, John; Ahmadinia, Ali.

Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. Springer-Verlag, 2009.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - The work presented in this paper addresses the application of new technologies to the task of intruder monitoring. It presents an innovative Machine Vision application to detect and track a person in a Closed Circuit Television System (CCTV) identifying suspicious activity. Neural Network techniques are applied to identify suspicious activities from the trajectory path, speed, direction and risk areas for a person in a scene, as well as human posture. Results correlate well with operator determining suspicious activity. The automated system presented assists an operator to increase reliability and to monitor large numbers of surveillance cameras.

AB - The work presented in this paper addresses the application of new technologies to the task of intruder monitoring. It presents an innovative Machine Vision application to detect and track a person in a Closed Circuit Television System (CCTV) identifying suspicious activity. Neural Network techniques are applied to identify suspicious activities from the trajectory path, speed, direction and risk areas for a person in a scene, as well as human posture. Results correlate well with operator determining suspicious activity. The automated system presented assists an operator to increase reliability and to monitor large numbers of surveillance cameras.

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