Paper Info
Title
An Evolutionary Optimization Based Interval Type-2 Fuzzy Classification System For Human Behaviour Recognition And Summarisation
Abstract
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 doesnot employ fuzzy classification techniques) and Type-1 FLSs (T1FLSs) counterparts in addition to non-fuzzy recognition methods.
Year
Venue
Keywords
2016
2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
Fuzzy Logic, behaviour recognition, 3D vision, evolutionary optimization
Field
DocType
ISSN
Fuzzy classification,Contextual awareness,Computer science,Fuzzy logic,Fuzzy set,Artificial intelligence,Big bang big crunch,Machine learning
Conference
1062-922X
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Bo Yao1232.61
Hani Hagras21747129.26
Jason J. Lepley300.34
Robert Peall400.34
Michael Butler500.34