BEWARE: Behaviour
based Enhancement of Wide-Area Situational Awareness in a Distributed
Network of CCTV Cameras
BEWARE
is a project funded by
EPSRC and
MOD to develop
models for video-based people tagging (consistent labelling) and behaviour monitoring across a distributed
network of CCTV cameras for the enhancement of global situational
awareness in a wide area.
There are
now large networks of CCTV cameras collecting colossal amounts of video
data, of which many deploy not only fixed but also mobile cameras on
wireless connections with an increasing number of the cameras being
either PTZ controllable or embedded smart cameras. A multi-camera
system has the potential for gaining better viewpoints resulting in
both improved imaging quality and more relevant details being captured.
However, more is not necessarily better. Such a system can also cause
overflow of information and confusion if data content is not analysed
in real-time to give the correct camera selection and capturing
decision. Moreover, current PTZ cameras are mostly controlled manually
by operators based on ad hoc criteria.
BEWARE aims to develop
automated systems to monitor behaviours of people cooperatively across
a distributed network of cameras and making on-the-fly decisions for
more effective content selection in data capturing. Specifically,
we are:
- Developing a model for robust
detection and tagging of people over wide areas of different physical
sites captured by a distributed network of cameras, e.g. monitoring the
activities of a person travelling through a city/cities.
- Developing a model for global
situational awareness enhancement via correlating behaviours across a
network of cameras located at different physical sites, and for
real-time detection of abnormal behaviours in public space across
camera views; The model must be able to cope with changes in visual
context and on definitions of abnormality, e.g. what is abnormal needs
be modelled by the time of the day, locations, and scene context.
- Developing a model for automatic
selection and controlling of Pan-Tilt-Zoom (PTZ) and embedded smart
cameras (including wireless ones) in a surveillance network to zoom
into people based on behaviour analysis using a global
situational awareness model therefore achieving active sampling of
higher quality visual evidence on the fly in a global context, e.g.
when a car enters a restricted zone which has also been spotted
stopping unusually elsewhere, the optimally situated PTZ and embedded
smart camera is to be activated to perform adaptive image content
selection and capturing of higher resolution imagery of, e.g. the face
of the driver.
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