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Title: Recognition of complex human activities in multimedia streams using machine learning and computer vision
Author: Kaloskampis, Ioannis
Awarding Body: Cardiff University
Current Institution: Cardiff University
Date of Award: 2013
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Modelling human activities observed in multimedia streams as temporal sequences of their constituent actions has been the object of much research effort in recent years. However, most of this work concentrates on tasks where the action vocabulary is relatively small and/or each activity can be performed in a limited number of ways. In this Thesis, a novel and robust framework for modelling and analysing composite, prolonged activities arising in tasks which can be effectively executed in a variety of ways is proposed. Additionally, the proposed framework is designed to handle cognitive tasks, which cannot be captured using conventional types of sensors. It is shown that the proposed methodology is able to efficiently analyse and recognise complex activities arising in such tasks and also detect potential errors in their execution. To achieve this, a novel activity classification method comprising a feature selection stage based on the novel Key Actions Discovery method and a classification stage based on the combination of Random Forests and Hierarchical Hidden Markov Models is introduced. Experimental results captured in several scenarios arising from real-life applications, including a novel application to a bridge design problem, show that the proposed framework offers higher classification accuracy compared to current activity identification schemes.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QA76 Computer software ; TA Engineering (General). Civil engineering (General)