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.
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Fernandez-Canque, H., Hintea, S., Freer, J., Ahmadinia, A. (2009). Machine Vision Application to Automatic Intruder Detection Using CCTV. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04592-9_62
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DOI: https://doi.org/10.1007/978-3-642-04592-9_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04591-2
Online ISBN: 978-3-642-04592-9
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