-Caveat Lector- http://www.newscientist.com/ns/19991211/warningstr.html New Scientist, 11 December 1999 Warning! Strange behaviour Nobody sees the thief looking for a car to break into, or the woman steeling herself to jump in front of a train -- but somehow the alarm is sounded. Duncan Graham-Rowe enters a world where machines predict our every move 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's 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--by lurking in shadow, perhaps--the 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--the computer recognises them as patterns. If anyone deviates from these patterns, the system sounds the alarm. "It's unusual for someone to hang around cars," says Maybank. "There are exceptions, but it's rare." 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't. 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. "If it's tall and thin it's a person," says Maybank. "If it's long and low it's a car." It's the trajectory of these "objects" that the system follows. An operator can constantly update the computer's notion of "normal behaviour" 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's 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--though this hasn't been tested yet. Of course, not all unusual activity is criminal. But if the system flags up an innocuous event, says Maybank, it doesn't really matter. The idea is to simply notify the Georges of this world when something out of the ordinary happens. It's 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's 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't. Most impressive of all, Cromatica can identify people who are about to throw themselves in front of a train. Frank Norris, the coroner's 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--people, that is. Instead, Cromatica identifies movement by monitoring the changing colours and intensities of the pixels that make up a camera's view of a platform. If the pixels are changing, the reasoning goes, the chances are that something is moving and that it's 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's likely that the crowd has stopped and there may be dangerous congestion--so an alarm would sound. Averting a tragedy Cromatica's 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's 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. "If we know there is a blob on the screen and it remains stationary for more than a few minutes then we raise the alarm," 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--it 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--such as congestion--spotted 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's present project, which includes partners such as the defence and communications company Racal, aims to detect other forms of criminal activity, "anything for which eventually you would want to call the police", 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--the capital's main business area--CCTV cameras are so widespread that it's 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 "tail" people remotely, logging automatically their meetings and other activities. Maybank and his colleagues worry about this type of use. "This is something that will have to be considered by society as a whole," he says. Simon Davies, director of the human rights group Privacy International, is scathing about the technology. "This is a very dangerous step towards a total control society," he says. For one thing, somebody has to decide what "normal behaviour" is, and that somebody is likely to represent a narrow, authoritarian viewpoint. "The system reflects the views of those using it," 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. "The push to conformity will be extraordinary," Davies says. "Young people will feel more and more uncomfortable if that sort of technology becomes ubiquitous." 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: "Suspect package alert. Suspect pack..." He flicks a switch. The system has zoomed in on a small bag on the ground next to a bench. "Where is it?" he demands. "Terminal three, departure gate 32," squawks the computer. "How long?" "Four minutes." "Show event," 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. "Where is he now?" asks George. "Terminal three, level 2, departure lounge." "Show me." The screen changes again, this time showing the man walking quickly towards the exit. George picks up his radio: "Jim. We've got a two-zero-three coming your way. Red shirt, black denim jacket. Pick him up." 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't. 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--apart from speech recognition. "We just haven't put it all together yet, so I don't want to say we can definitely do it now," he says. Grimson's system, which is partly funded by the US government's 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 "shown" where the floor and walls are. Grimson's system does this automatically. And provided there is a little bit of overlap between the cameras' images, FoS will figure out where in the big scheme of things every image belongs. "We do it purely on the basis of moving objects," he says. "As long as we can track anything in sight, we can use that information to help the system figure out where all the cameras are." 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's 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't understand what these activities are, says Grimson, it merely categorises activities by learning from vast numbers of examples. It's 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's what he's working on now. Higher resolution should also allow him to exploit progress made in recent years in gesture recognition. In particular, he thinks that "gait recognition" will make its mark as a way to identify people. It needs lower resolution than face recognition and its reliability is growing fast (New Scientist, 4 December, p 18). http://www.newscientist.com/ns/19991204/newsstory3.html 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. "You have to rely on the legal system to strike a balance," he says. "It is a real worry." Fortunately, both these tasks are probably impractical at present. "The volume of data is so huge it's incredibly unlikely," 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's 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't 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's home. Is this, then, an unequivocally good use for a smart surveillance system? Davies reckons not. "This is like justifying road accidents because they provide hospital beds," 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're already starting to recognise people's faces in the street (New Scientist, 25 September, p 40 -- http://www.newscientist.com/ns/19991204/newsstory3.html ), and systems that spot abnormal behaviour will not be far behind. So, if you have a hanker- ing to cartwheel down main street you'd better do it now. Wait a few years and it will be recorded, annotated and stored--just waiting to come back and haunt you. Further reading: For more information about Hogg and Maybank's work, see: www.cvg.cs.rdg.ac.uk/papers/list.html Details of Velastin's research are at: www.research.eee.kcl.ac.uk/~vrl/ Information about the Forest of Sensors is at: www.ai.mit.edu/projects/vsam/ � Copyright New Scientist, RBI Limited 1999 . DECLARATION & DISCLAIMER ========== CTRL is a discussion and informational exchange list. Proselyzting propagandic screeds are not allowed. Substance�not soapboxing! These are sordid matters and 'conspiracy theory', with its many half-truths, misdirections and outright frauds is used politically by different groups with major and minor effects spread throughout the spectrum of time and thought. 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