快猫短视频

GEORGE is blissfully unaware that a crime is about to be committed right
under his nose. Partially obscured by a bag of doughnuts and a half-read
newspaper is one of the dozens of security monitors he is employed to watch
constantly for thieves and vandals.

On the screen in question, a solitary figure furtively makes his way through
a car park towards his target. The miscreant knows that if the coast is clear it
will take him maybe 10 seconds to get into the car, 15 to bypass the engine
immobiliser and 10 to start the engine. Easy.

But before he has even chosen which car to steal, an alarm sounds in the
control room, waking George from his daydream. A light blinking above the screen
alerts him to the figure circling in the car park and he picks up his radio. If
his colleagues get there quickly enough, they will not only catch a villain but
also prevent a crime.

The unnatural prophetic powers of the security team would not exist but for
some smart technology. The alarm that so rudely disturbed George is part of a
sophisticated visual security system that predicts when a crime is about to be
committed. The remarkable research prototype was developed by Steve Maybank at
the University of Reading and David Hogg at the University of Leeds. Although in
its infancy, this technology could one day be used to spot shoplifters, predict
that a mugging is about to take place on a subway or that a terrorist is active
at an airport.

Once connected to such intelligent systems, closed-circuit television
(CCTV) will shift from being a mainly passive device for gathering evidence
after a crime, to a tool for crime prevention. But not everyone welcomes the
prospect. The technology would ensure that every security screen is closely
watched, though not by human eyes. It would bring with it a host of sinister
possibilities and fuel people鈥檚 fears over privacy.

Criminals certainly have reason to be worried, with the car park system, for
example, the more thieves try to hide from a camera鈥攂y lurking in shadow,
perhaps鈥攖he easier it is to spot them. Underlying the system is the fact
that people behave in much the same way in car parks. Surprisingly, the pathways
they follow to and from their cars are so similar as to be mathematically
predictable鈥攖he computer recognises them as patterns. If anyone deviates
from these patterns, the system sounds the alarm. 鈥淚t鈥檚 unusual for someone to
hang around cars,鈥� says Maybank. 鈥淭here are exceptions, but it鈥檚 rare.鈥�

To fool the system, a thief would have to behave as though they owned the
car, confidently walking up to it without casing it first or pausing to see if
the real owner is nearby. In short, they have to stop behaving like a thief. It
sounds easy, but apparently it isn鈥檛.

Another surprising thing about the system is that it employs relatively
unsophisticated technology. For decades, researchers have been devising clever
ways for a computer presented with a small section of a face, arm or leg to
deduce that it is looking at a person. Maybank and Hogg have rejected all this
work, giving their prototype only the simplest of rules for recognising things.
鈥淚f it鈥檚 tall and thin it鈥檚 a person,鈥� says Maybank. 鈥淚f it鈥檚 long and low it鈥檚
a car.鈥�

It鈥檚 the trajectory of these 鈥渙bjects鈥� that the system follows. An operator
can constantly update the computer鈥檚 notion of 鈥渘ormal behaviour鈥� by changing a
series of threshold values for such things as the width of pathways and walking
speed. In this way it can be made more reliable over time. If trained on enough
suitable footage, the system should be able to view children running in the car
park or somebody tinkering with their engine without raising the alarm. Its
ability to calculate where people are likely to go even allows the system to
predict which car a thief is aiming for, though Maybank concedes that the
crook鈥檚 target cannot be guaranteed.

The system should identify more than just potential car thieves. Because it
spots any abnormal behaviour, the computer should sound the alarm if a fight
breaks out鈥攖hough this hasn鈥檛 been tested yet. Of course, not all unusual
activity is criminal. But if the system flags up an innocuous event, says
Maybank, it doesn鈥檛 really matter. The idea is to simply notify the Georges of
this world when something out of the ordinary happens. It鈥檚 up to them to decide
whether or not they need to act on what they see.

Maybank plans now to join forces with Sergio Velastin of King鈥檚 College
London and others in a project funded by the European Commission to develop a
full range of security features for subways. Velastin has already broken new
ground in this area. In a recently completed project, called Cromatica, he
developed a prototype that has been tested on the London Underground for
monitoring crowd flows and warning of dangerous levels of congestion. It will
also spot people behaving badly, such as those going where they shouldn鈥檛.

Most impressive of all, Cromatica can identify people who are about to throw
themselves in front of a train. Frank Norris, the coroner鈥檚 liaison officer for
London Underground, says there is an average of one suicide attempt on the
network every week. These incidents are not only personal tragedies but also
cause chaos for millions of commuters and great distress for the hapless train
drivers.

Keeping track of thousands of people in a tube station is impossible for a
human or a computer. Following individuals is tough enough: as people move,
different parts of their bodies appear and disappear, and sometimes they are
completely obscured. To get round this problem, Velastin rejected completely the
idea of identifying objects鈥攑eople, that is.

Instead, Cromatica identifies movement by monitoring the changing colours and
intensities of the pixels that make up a camera鈥檚 view of a platform. If the
pixels are changing, the reasoning goes, the chances are that something is
moving and that it鈥檚 human. The system compares its view second by second with
what it sees when the platform is empty. The more its view changes from this
baseline, the more people are passing, and the speed of change gives a measure
of how quickly those people are moving. If things stay constant for too long,
it鈥檚 likely that the crowd has stopped and there may be dangerous
congestion鈥攕o an alarm would sound.

Averting a tragedy

Cromatica鈥檚 ability to spot people contemplating suicide stems from the
finding, made by analysing previous cases, that these individuals behave in a
characteristic way. They tend to wait for at least ten minutes on the platform,
missing trains, before taking their last few tragic steps. Velastin鈥檚
deceptively simple solution is to identify patches of pixels that are not
present on the empty platform and which stay unchanged between trains, once
travellers alighting at the station have left.

鈥淚f we know there is a blob on the screen and it remains stationary for more
than a few minutes then we raise the alarm,鈥� says Velastin. Security guards can
then decide whether or not they need to intervene. So far, Cromatica has not
seen video footage of real suicide cases鈥攊t has only identified people who
have simulated the behaviour.

In trials where Cromatica was pitted against humans it proved itself
dramatically, detecting 98 per cent of the events鈥攕uch as
congestion鈥攕potted by humans. In fact, the humans performed
unrealistically well in the tests because they had to watch just one screen,
whereas they would normally check several screens at once. Cromatica also scored
well on false alarms: only 1 per cent of the incidents it flagged up turned out
to be non-events. This low rate is vital, says Velastin, if operators are to
trust the system.

Velastin and Maybank鈥檚 present project, which includes partners such as the
defence and communications company Racal, aims to detect other forms of criminal
activity, 鈥渁nything for which eventually you would want to call the police鈥�,
says Velastin. This will include people selling tickets illegally and any
violent behaviour.

But detecting violent crime is not as straightforward as it might appear.
Certainly if a fight breaks out the characteristic fast, jerky movements of
fists flying and bodies grappling would show up as unusual activity. But what of
a mugging? Often a mugging is a verbal confrontation with no physical contact.
To a vision system, someone threatening a person with a knife looks much the
same as someone offering a cigarette to a friend. Indeed, recognising that there
is any interaction at all between people is still a monster challenge for a
machine. No one yet has the answer.

Nevertheless, Maybank is taking the first tentative steps into this field,
incorporating into his car park system a method for identifying what people are
doing and then annotating the videotape with the details. The technique works by
attaching virtual labels to objects, such as cars and people, and then analysing
the way they move and interact. So far the system can distinguish between basic
activities such as walking, driving and meeting (or mugging).

It is here, provided the system can be perfected, that Maybank sees the
potential for sinister uses of the technology. In places such as the City of
London鈥攖he capital鈥檚 main business area鈥擟CTV cameras are so
widespread that it鈥檚 difficult to avoid them. With such blanket coverage, and as
it becomes possible to track a person from one camera to the next, it would be
relatively easy to 鈥渢ail鈥� people remotely, logging automatically their meetings
and other activities. Maybank and his colleagues worry about this type of use.
鈥淭his is something that will have to be considered by society as a whole,鈥� he
says.

Simon Davies, director of the human rights group Privacy International, is
scathing about the technology. 鈥淭his is a very dangerous step towards a total
control society,鈥� he says. For one thing, somebody has to decide what 鈥渘ormal
behaviour鈥� is, and that somebody is likely to represent a narrow, authoritarian
viewpoint. 鈥淭he system reflects the views of those using it,鈥� he argues. Anyone
who does act out of the ordinary will be more likely than now to be approached
by security guards, which will put pressure on them to avoid standing out. 鈥淭he
push to conformity will be extraordinary,鈥� Davies says. 鈥淵oung people will feel
more and more uncomfortable if that sort of technology becomes ubiquitous.鈥�

On the other hand, to fully grasp the benefits of a system that can recognise
and record details of different activities, consider the following scenario: a
future, technology-savvy George keeps watch as thousands of people flow through
an airport. The security team has been tipped off about a terrorist threat. But
where to begin?

One starting point is to watch for unattended baggage. Most airports do this
continuously, with the majority of cases turning out to be lost luggage. So how
do you distinguish between a lost item and one deliberately abandoned? The best
way would be if George could rewind to the precise moment when a bag was left by
its owner.

George takes a bite of doughnut and washes it down with some tepid coffee
when suddenly an alarm sounds:

鈥淪uspect package alert. Suspect pack鈥︹�� He flicks a switch. The system has
zoomed in on a small bag on the ground next to a bench.

鈥淲here is it?鈥� he demands.

鈥淭erminal three, departure gate 32,鈥� squawks the computer.

鈥淗ow long?鈥�

鈥淔our minutes.鈥�

鈥淪how event,鈥� orders George.

The system searches back until it finds the electronic annotation that marks
where the bag and its carrier parted company. The screen changes to show a man
sitting on the bench with the bag at his feet. He reaches into it briefly, looks
around, then stands and walks away.

鈥淲here is he now?鈥� asks George.

鈥淭erminal three, level 2, departure lounge.鈥�

鈥淪how me.鈥�

The screen changes again, this time showing the man walking quickly towards
the exit. George picks up his radio: 鈥淛im. We鈥檝e got a two-zero-three coming
your way. Red shirt, black denim jacket. Pick him up.鈥� After alerting the bomb
squad and clearing the departure gate, he pops the remainder of the doughnut
into his mouth and turns back to that pesky crossword . . .

Seamless tracking

There are plenty of instances where it would be helpful to refer back to
specific events. And though this scenario may sound far-fetched, it isn鈥檛. The
Forest of Sensors (FoS), developed by Eric Grimson at the Massachusetts
Institute of Technology, near Boston, already has all the foundations of such a
system鈥攁part from speech recognition. 鈥淲e just haven鈥檛 put it all together
yet, so I don鈥檛 want to say we can definitely do it now,鈥� he says.

Grimson鈥檚 system, which is partly funded by the US government鈥檚 Defense
Advanced Research Projects Agency, sets itself up from scratch with no human
intervention. The idea behind it was that dozens of miniature cameras could be
dropped at random into a military zone and FoS would work out the position of
every camera and build up a three-dimensional representation of the entire area.
The result is a network of cameras that requires no calibration whatsoever. You
simply plug and play, says Grimson.

Quick and dirty

In order to build up a three-dimensional image, most 3D surveillance systems,
such as those used in the car park and subway, need every camera to be 鈥渟hown鈥�
where the floor and walls are. Grimson鈥檚 system does this automatically. And
provided there is a little bit of overlap between the cameras鈥� images, FoS will
figure out where in the big scheme of things every image belongs.

鈥淲e do it purely on the basis of moving objects,鈥� he says. 鈥淎s long as we can
track anything in sight, we can use that information to help the system figure
out where all the cameras are.鈥� Having decided what is background movement, such
as clouds passing or trees blowing in the wind, FoS then assumes that other
objects are moving on the ground. From these movements, it calculates the ground
plane and reconstructs the 3D space it鈥檚 looking at. The system then allows
seamless tracking from one camera to the next.

FoS is smart in other ways too. The system can learn from what it sees and
build up a profile of what is and what is not normal behaviour. It
differentiates between objects by sensing their shapes, using quick-and-dirty
methods to detect their edges and measure their aspect ratios. It then
classifies them as, for example, individuals, groups of people, cars, vans,
trucks, cyclists and so on.

Moreover, the system can employ its inbuilt analytical powers to decide for
itself what activities the camera is seeing, such as a person getting into a car
or loading a truck. Of course, the system doesn鈥檛 understand what these
activities are, says Grimson, it merely categorises activities by learning from
vast numbers of examples. It鈥檚 up to a human to give each activity a name.

Like Maybank and Hogg, Grimson is still struggling to distinguish a meeting
from a mugging. He hopes that higher resolution cameras, that can spot small
details and movements, will help to crack the problem, and that鈥檚 what he鈥檚
working on now. Higher resolution should also allow him to exploit progress made
in recent years in gesture recognition. In particular, he thinks that 鈥済ait
recognition鈥� will make its mark as a way to identify people. It needs lower
resolution than face recognition and its reliability is growing fast (New
快猫短视频, 4 December, p 18).

FoS can already perform many of the tasks that gives Maybank the jitters.
Grimson, too, has reservations about what his research might be used for. His
system could conceivably be used by intelligence agencies to monitor the
behaviour of individuals. But he would be unhappy if his research were used in
this way. 鈥淵ou have to rely on the legal system to strike a balance,鈥� he says.
鈥淚t is a real worry.鈥� Fortunately, both these tasks are probably impractical at
present. 鈥淭he volume of data is so huge it鈥檚 incredibly unlikely,鈥� he says.

One place where Grimson is keen to deploy FoS is in the homes of elderly
people. Many old folk are unhappy about being monitored in their homes by CCTV
because of the lack of privacy, he says. But with FoS, there would be no need
for a human to watch at all. The system would train itself on a person鈥檚
patterns of behaviour and ask them if they were all right if they failed to get
up one morning or fell over. If the person didn鈥檛 respond, the system would
issue a distress call to a help centre. Another George would send someone round
to help, without even once seeing inside the person鈥檚 home.

Is this, then, an unequivocally good use for a smart surveillance system?
Davies reckons not. 鈥淭his is like justifying road accidents because they provide
hospital beds,鈥� he says. Elderly people will end up trying to conform to the
system so as not to trigger the alarm.

But, whether for good or bad, surveillance machines are going to to get
smarter. They鈥檙e already starting to recognise people鈥檚 faces in the street
(快猫短视频, 25 September, p 40), and systems that spot
abnormal behaviour will not be far behind. So, if you have a hankering to
cartwheel down main street you鈥檇 better do it now. Wait a few years and it will
be recorded, annotated and stored鈥攋ust waiting to come back and haunt you.