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An evolutionary optimization based interval type-2 fuzzy classification system for human behaviour recognition and summarisation

Yao, B and Hagras, H and Lepley, JJ and Peall, R and Butler, M (2017) An evolutionary optimization based interval type-2 fuzzy classification system for human behaviour recognition and summarisation. In: UNSPECIFIED, ? - ?.

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Abstract

© 2016 IEEE. Automatic recognition of behaviours and events from visual data is an emerging topic in video surveillance. These methods promise the ability to derive contextual awareness for a scene and may further enable the ability to predict the intentions of the subject. This paper describes a novel system for analysing human behaviours in the context of a video surveillance application. This may be used to distinguish between normal and anomalous behaviours. We propose a novel framework for the application of behaviour recognition and summarisation using interval type-2 fuzzy logic classification systems (IT2FLS). We employ the evolutionary-based technique Big Bang Big Crunch (BB-BC) to automatically optimise parameters of membership functions (MFs) and rules in the IT2FLSs. Our analysis shows that the BB-BC IT2FLS is able to robustly recognise behaviours and furthermore outperforms both its' conventional IT2FLS (which does not employ fuzzy classification techniques) and Type-1 FLSs (T1FLSs) counterparts in addition to non-fuzzy recognition methods.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 20 Jun 2017 15:09
Last Modified: 05 Feb 2019 18:16
URI: http://repository.essex.ac.uk/id/eprint/19900

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