[AI] Smart CCTV learns to spot suspicious types

Sanjay ilovecold at gmail.com
Tue Apr 20 02:36:51 EDT 2010

 Smart CCTV learns to spot suspicious types
          Video-analysis software can follow people from camera to camera in
          busy public places and identify those who are behaving

by Nic Fleming

WHAT'S the difference between a suicide bomber and a cleaner? It
sounds like the opening line of a sick joke, but for computer
scientists working on intelligent video-surveillance software, being
able to make that distinction is a key goal.

Current CCTV systems can collect masses of data, but little of it is
used, says Shaogang Gong, a computer-vision computation researcher
at Queen Mary, University of London. "What we really need are better
ways to mine that data," he says.

Gong is leading an international team of researchers to develop a
next-generation CCTV system, called Samurai, which is capable of
identifying and tracking individuals that act suspiciously in crowded
public spaces. It uses algorithms to profile people's behaviour,
learning about how people usually behave in the environments where it
is deployed. It can also take changes in lighting conditions into
account, enabling it to track people as they move from one camera's
viewing field to another.

To improve the tracking of an individual at an airport, the system can
also learn the routes people are likely to take - straight from the
entrance to check-in, say. It can even follow a target as they move in
a crowd, using the characteristic shape of the person, their luggage
and the people they are walking with, to follow them as they walk
between different camera views.

Samurai is designed to issue alerts when it detects behaviour that
differs from the norm, and adjusts its reasoning based on feedback. So
an operator might reassure the system that the person with a mop
appearing to loiter in a busy thoroughfare is no threat. When another
person with a mop exhibits similar behaviour, it will remember that
this is not a situation that needs flagging up.

While video analysis tools already exist, they tend to operate
according to rigid, predefined rules, says Gong, and cannot follow a
large number of people across multiple cameras situated in busy public

The Samurai team last month demonstrated the system to commercial
partners including BAA Airports in the UK. The researchers claim the
prototype system successfully identified potential threats which may
have been missed by human operators, using footage collected at
Heathrow airport. The Samurai team has funding to continue refining
their software until the end of 2011.

"The use of relevant feedback from human operators will be a very
important part of these technologies," says Paul Miller, of
Queen's University's Centre for Secure Information Technologies in
Belfast, UK, who is leading a project to develop a video-analysis
system capable of predicting assaults on buses. "The key is developing
learning algorithms that work not only in the lab but that are robust
in real-world applications."

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