Owens, Jonathan and Hunter, Andrew and Fletcher, Eric (2002) Novelty detection in video surveillance using hierarchical neural networks. In: ICANN, 27-30 August 2002, Madrid, Spain.

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Abstract

Abstract. A hierarchical self-organising neural network is described for the detection of unusual pedestrian behaviour in video-based surveillance systems. The system is trained on a normal data set, with no prior information about the scene under surveillance, thereby requiring minimal user input. Nodes use a trace activation rule and feedforward connections, modified so that higher layer nodes are sensitive to trajectory segments traced across the previous layer. Top layer nodes have binary lateral connections and corresponding “novelty accumulator” nodes. Lateral connections are set between co-occurring nodes, generating a signal to prevent accumulation of the novelty measure along normal sequences. In abnormal sequences the novelty accumulator nodes are allowed to increase their activity, generating an alarm state.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Abstract. A hierarchical self-organising neural network is described for the detection of unusual pedestrian behaviour in video-based surveillance systems. The system is trained on a normal data set, with no prior information about the scene under surveillance, thereby requiring minimal user input. Nodes use a trace activation rule and feedforward connections, modified so that higher layer nodes are sensitive to trajectory segments traced across the previous layer. Top layer nodes have binary lateral connections and corresponding “novelty accumulator” nodes. Lateral connections are set between co-occurring nodes, generating a signal to prevent accumulation of the novelty measure along normal sequences. In abnormal sequences the novelty accumulator nodes are allowed to increase their activity, generating an alarm state.
Keywords:	Video Surveillance, Neural network, Surveillance
Subjects:	G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User:	Tammie Farley
Date Deposited:	21 Jul 2009 07:55
Last Modified:	13 Mar 2013 08:32
URI:	http://eprints.lincoln.ac.uk/id/eprint/1900

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