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An extended fuzzy SOM for anomalous behaviour detection | IEEE Conference Publication | IEEE Xplore

An extended fuzzy SOM for anomalous behaviour detection


Abstract:

Analysis of motion patterns is an effective approach for gaining better understanding of human behaviour. Many methods have been proposed to tackle this problem. However,...Show More

Abstract:

Analysis of motion patterns is an effective approach for gaining better understanding of human behaviour. Many methods have been proposed to tackle this problem. However, unsupervised approaches have been widely accepted for clustering motion patterns, due to the fact that no previous knowledge of the scene is required. The fuzzy self-organizing map (fuzzy SOM) is an unsupervised method which has been previously used for classifying motion patterns. However, it suffers from high computational cost when a large number of output neurons is required, especially with complex scenes. In this paper, we propose a novel approach for dealing with the number of output neurons of fuzzy SOM in a complex scene. The performance of our approach shows better results compared with the normal approach, and without any major effect on the computational cost.
Published in: CVPR 2011 WORKSHOPS
Date of Conference: 20-25 June 2011
Date Added to IEEE Xplore: 11 August 2011
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Conference Location: Colorado Springs, CO, USA

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