Details of Grant 

EPSRC Reference:	EP/G063974/1
Title:	Multi-Object Video Behaviour Modelling for Abnormality Detection and Differentiation
Principal Investigator:	Xiang, Dr T
Other Investigators:
Researcher Co-Investigators:
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Department:	Sch of Electronic Eng & Computer Science
Organisation:	Queen Mary University of London
Scheme:	First Grant Scheme
Starts:	01 September 2009	Ends:	31 October 2012	Value (£):	350,507
EPSRC Research Topic Classifications:	Image & Vision Computing
EPSRC Industrial Sector Classifications:	No relevance to Underpinning Sectors
Related Grants:
Panel History:	Panel Date Panel Name Outcome 04 Mar 2009 ICT Prioritisation Panel (March 09) Announced
Summary on Grant Application Form
There are over 4.2 million closed-circuit television (CCTV) surveillance cameras operational in the UK and many more worldwide, collecting a colossal amount of video data for security, safety, and infrastructure and facility management purposes. A typical existing CCTV system relies on a handful of human operators at a centralised control room for monitoring video inputs from hundreds of cameras. Too many cameras and too few operators leave the system ill equipped to fulfil the task of detecting events and anomalies that require immediate and appropriate response. Consequently, the use of the existing CCTV surveillance systems is limited predominately to post-mortem analysis. There is thus an increasing demand for automated intelligent systems for analysing the content of the vast quantities of surveillance videos and triggering alarms in a timely and robust fashion. One of the most critical components and functionalities of such a system is to monitor object behaviour captured in the videos and detect/predict any suspicious and abnormal behaviour that could pose a threat to public safety and security. This project aims to develop underpinning capabilities for an innovative intelligent video analytics system for detecting abnormal video behaviour in public spaces. More specifically, the project will address three open problems:1.To develop a new model for spatio-temporal visual context for abnormal behaviour detection. Behaviours are inherently context-aware, exhibited through constraints imposed by scene layout and the temporal nature of activities in a given scene. Consequently, the same behaviour can be deemed as either normal or abnormal depending on where and when it occurs. We aim to go beyond the state-of-the-art semantic scene modelling approaches, most of which are focused solely on modelling scene layout such as entry and exit points, by developing a more comprehensive spatio-temporal model of dynamic visual context. 2.To develop a novel multi-object behaviour model for real-time detection and differentiation of abnormalities in complex video behaviours that involve multiple objects interacting with each other (e.g. a group of people meet in front of a ticket office at a train station and then go to different platforms). 3.To develop a novel online adaptive learning algorithm for estimating the parameters of the behaviour model to be developed. Although video abnormality detection tools are already available in many existing CCTV control systems, human operators are often reluctant to use them because there are too many parameters to tune and re-tune for different scenarios given changing visual context. With the incremental and adaptive learning algorithm our behaviour model can be used for different surveillance scenarios over a long period of time with minimal human intervention. More importantly, using the algorithm, our behaviour model will become adaptive to both changes of visual context (therefore the definition of normality/abnormality), and valuable feedbacks from human operators on the abnormality detection output of the model.
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