An evolutionary optimization based interval type-2 fuzzy classification system for human behaviour recognition and summarisation
Bo Yao, Hani Hagras, Jason J. Lepley, Robert Peall, Michael Butler · 2016
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.