Automatic number plate recognition (ANPR; see also other names below) is a mass surveillance method that uses optical character recognition on images to read the licence plates on vehicles. As of 2006, systems can scan number plates at around one per second on cars travelling up to 100 mph (160 km/h). They can use existing closed-circuit television or road-rule enforcement cameras, or ones specifically designed for the task. They are used by various police forces and as a method of electronic toll collection on pay-per-use roads, and monitoring traffic activity such as red light adherence in an intersection.
ANPR can be used to store the images captured by the cameras as well as the text from the licence plate, with some configurable to store a photograph of the driver. Systems commonly use infrared lighting to allow the camera to take the picture at any time of day. A powerful flash is included in at least one version of the intersection-monitoring cameras, serving to both illuminate the picture and make the offender aware of his or her mistake. ANPR technology tends to be region specific, owing to plate variation from place to place.
Concerns about these systems have centered on privacy fears of government tracking citizens' movements and media reports of misidentification and high error rates. However, as they have developed, the systems have become much more accurate and reliable.
ANPR uses optical character recognition (OCR) on images taken by cameras. When Dutch vehicle registration plates switched to a different style in 2002 one of the changes made was to the font, introducing small gaps in some letters (such as P and R) to make them more distinct and therefore more legible to such systems. Some licence plate arrangements use variations in font sizes and positioning – ANPR systems must be able to cope with such differences in order to be truly effective. More complicated systems can cope with international variants, though many programs are individually tailored to each country.
The cameras used can include existing road-rule enforcement or closed-circuit television cameras as well as mobile units which are usually attached to vehicles. Some systems use infrared cameras to take a clearer image of the plates.
Despite their effectiveness, there are noteworthy challenges related with mobile ANPRs. One of the biggest is that the processor and the cameras must work fast enough to accommodate relative speeds of more than 100 mph, a likely scenario in the case of oncoming traffic. This equipment must also be very efficient since the power source is the vehicle battery and equipment must be small to minimize the space it requires.
Relative speed is only one issue that affects the camera’s ability to actually read a license plate. Algorithms must be able to compensate for all the variables that can affect the ANPR’s ability to produce an accurate read, such as time of day, weather and angles between the cameras and the license plates. A system’s illumination wavelengths can also have a direct impact on the resolution and accuracy of a read in these conditions.
Installing ANPR cameras on law enforcement vehicles requires careful consideration of the juxtaposition of the cameras to the license plates they are to read. Using the right number of cameras and positioning them accurately for optimal results can prove challenging given the various missions and environments at hand. Highway patrol requires forward-looking cameras that span multiple lanes and are able to read license plates at very high speeds. City patrol needs shorter range, lower focal length cameras for capturing plates on parked cars. And parking lots with perpendicularly parked cars often require a specialized camera with a very short focal length. Most technically advanced systems are flexible and can be configured with a number of cameras ranging from one to four which can easily be repositioned as needed. States with rear-only license plates have an additional challenge since a forward-looking camera is in effective with incoming traffic. In this case one camera may be turned backwards.
There are six primary algorithms that the software requires for identifying a licence plate:
The complexity of each of these subsections of the program determines the accuracy of the system. During the third phase (normalisation) some systems use edge detection techniques to increase the picture difference between the letters and the plate backing. A median filter may also be used to reduce the visual "noise" on the image.
There are a number of possible difficulties that the software must be able to cope with. These include:
While some of these problems can be corrected within the software it is primarily left to the hardware side of the system to work out solutions to these difficulties. Increasing the height of the camera may avoid problems with objects (such as other vehicles) obscuring the plate, but introduces and increases other problems such as the adjusting for the increased skew of the plate.
On some cars, towbars may obscure one or two characters of the licence plate. Bikes on bike racks can also obscure the number plate, though in some countries and jurisdictions, such as Victoria, Australia, "bike plates" are supposed to be fitted.
Some small-scale systems allow for some errors in the licence plate. When used for giving specific vehicles access to a barriered area the decision may be made to have an acceptable error rate of one character. This is because the likelihood of an unauthorised car having such a similar licence plate is seen as quite small. However, this level of inaccuracy would not be acceptable in most applications of an ANPR system.
At the front end of any ANPR system is the imaging hardware which captures the image of the license plates. The initial image capture forms a critically important part of the ANPR system which, in accordance to the Garbage In, Garbage Out principle of computing, will often determine the overall performance.
License plate capture is typically performed by specialized cameras designed specifically for the task. Factors which pose difficulty for license plate imaging cameras include speed of the vehicles being recorded, varying ambient lighting conditions, headlight glare and harsh environmental conditions. Most dedicated license plate capture cameras will incorporate infrared illumination in order to solve the problems of lighting and plate reflectivity.
Many countries now use licence plates that are retroreflective. This returns the light back to the source and thus improves the contrast of the image. In some countries, the characters on the plate are not reflective, giving a high level of contrast with the reflective background in any lighting conditions. A camera that makes use of active infrared imaging (with a normal colour filter over the lens and an infrared illuminator next to it) benefits greatly from this as the infrared waves are reflected back from the plate. This is only possible on dedicated ANPR cameras, however, and so cameras used for other purposes must rely more heavily on the software capabilities. Further, when a full-colour image is required as well as use of the ANPR-retrieved details it is necessary to have one infrared-enabled camera and one normal (colour) camera working together.
To avoid blurring it is ideal to have the shutter speed of a dedicated camera set to 1/1000th of a second. Because the car is moving, slower shutter speeds could result in an image which is too blurred to read using the OCR software, especially if the camera is much higher up than the vehicle. In slow-moving traffic, or when the camera is at a lower level and the vehicle is at an angle approaching the camera, the shutter speed does not need to be so fast. Shutter speeds of 1/500th of a second can cope with traffic moving up to 40 mph (64 km/h) and 1/250th of a second up to 5 mph (8 km/h). License plate capture cameras can now produce usable images from vehicles traveling at .
To maximize the chances of effective license plate capture, installers should carefully consider the positioning of the camera relative to the target capture area. Exceeding threshold angles of incidence between camera lens and license plate will greatly reduce the probability of obtaining usable images due to distortion. Manufacturers have developed tools to help eliminate errors from the physical installation of license plate capture cameras
For the 407 toll route in Ontario, Canada, police have caught several advanced techniques that some motorists have attempted. One driver had a setup that allowed him to lift a wire from the driver's seat that would show a different plate as he was cruising through the camera zones . Other users have attempted to smear their license plate with dirt or utilize covers to mask the plate.
Novelty frames around Texas license plates were made illegal in Texas on 1 September 2003 by Texas Senate Bill 439 because they caused problems with ANPR devices. That law made it a Class C misdemeanor (punishable by a fine of up to US $200), or Class B (punishable by a fine of up to US $2,000 and 180 days in jail) if it can be proven that the owner did it to deliberately obscure their plates.
There are some custom car rear panels with an inset for the license plate at an angle, which changes the alignment of characters relative to the reading grid . Since most U.S. states no longer require new plates each year, perhaps the easiest way to disable recognition is simply to allow the reflective paint on the plates to become degraded by age and therefore unreadable .
If an ANPR system cannot read the plate it can flag the image for attention, with the human operators looking to see if they are able to identify the alphanumerics. It is then possible to do lookups on a database using wildcard characters for any part of the plate obscured, and use car details (make and model, for example) to refine the search.
In order to avoid surveillance or penalty charges, there has been an upsurge in car cloning, particularly in London . This is usually achieved by copying registration plates from another car of a similar model and age. This can be difficult to detect, especially as cloners may change the registration plates and travel behavior to hinder investigations.
In principle, it may be possible to foil infrared detection simply by heating the license plate to a sufficient temperature so that the infrared brightness of the license plate exceeds that of the reflected signal that would otherwise be detected in the camera.
One of the biggest challenges with ALPR technology in the US is the accuracy of the Optical Character Recognition (OCR)—the actual identification of the characters on the license plate. Many variables affect OCR accuracy, starting with the fact that each state has at least twelve license plate designs that must be recognized by the ALPR system. Also, the shape of the characters, color of the plates and whether the characters are raised or flat can affect accuracy. Many times the letter D is mistaken for a Q or an O and some colors, especially reddish tones, are hard to read
From time to time, states will make significant changes in their license plate protocol that will affect OCR accuracy. They may add a character or add a new license plate design. ALPR systems must adapt to these changes quickly in order to be effective.
Another challenge with ALPR systems is that some states have the same license plate protocol. For example more than one state may use three letters followed by four numbers. So each time the ALPR systems alarms, it is the user’s responsibility to make sure that the plate which caused the alarm matches the state associated with the license plate listed on the in-car computer.
Many states in America are deploying ALPR systems to aid a diverse range of missions. One of the most common is that of removing drivers with suspended or revoked licenses from the streets, because these drivers are more likely to cause accidents.
Another effective application of ALPR is the recovery of stolen vehicles. ALPR technology increases the odds of recovery because these systems can cross check license plate numbers against lists of stolen plates and vehicles many times faster than if done manually. This capability is very beneficial to the auto insurance industry and in Arizona, insurance companies are helping to fund the purchase of ALPR systems for their local law enforcement agencies.
Other ALPR missions include parking enforcement and identifying individuals who are delinquent on city or state taxes. By cracking down on these offenses, many cities and states are significantly increasing their revenue.
A recent initiative by New York State deployed LPR systems toward the mission of catching organized car thieves by tracing suspect plates back to forged documents.
In addition to the real-time processing of license plate numbers, some ALPR systems in the US collect data at the time of each license plate capture. Data such as date and time stamps and GPS coordinates can be reviewed in relation to investigations and can help lead to critical breaks such as placing a suspect at a scene, witness identification, pattern recognition or the tracking of suspect individuals
Another use for ANPR in the UK and in Italy is for speed cameras which work by tracking vehicles' travel time between two fixed points, and therefore calculate the average speed. These cameras are claimed to have an advantage over traditional speed cameras in maintaining steady legal speeds over extended distances, rather than encouraging heavy braking on approach to specific camera locations and subsequent acceleration back to illegal speeds.
The Tutor system is able to intercept also cars while changing lanes.
Many cities and districts have developed traffic control systems to help monitor the movement and flow of vehicles around the road network. This had typically involved looking at historical data, estimates, observations and statistics such as:
CCTV cameras can be used to help traffic control centres by giving them live data, allowing for traffic management decisions to be made in real-time. By using ANPR on this footage it is possible to monitor the travel of individual vehicles, automatically providing information about the speed and flow of various routes. These details can highlight problem areas as and when they occur and helps the centre to make informed incident management decisions.
Some counties of the United Kingdom have worked with Siemens Traffic to develop traffic monitoring systems for their own control centres and for the public. Projects such as Hampshire County Council's ROMANSE provide an interactive and real-time web site showing details about traffic in the city. The site shows information about car parks, ongoing road works, special events and footage taken from CCTV cameras. ANPR systems can be used to provide average driving times along particular routes, giving drivers the ability to choose which one to take. ROMANSE also allows travellers to see the current situation using a mobile device with an Internet connection (such as WAP, GPRS or 3G), thus allowing them to be alerted to any problems that are ahead.
The UK company Trafficmaster has used ANPR since 1998 to estimate average traffic speeds on non-motorway roads without the results being skewed by local fluctuations caused by traffic lights and similar. The company now operates a network of over 4000 ANPR cameras, but claims that only the four most central digits are identified, and no numberplate data is retained.<
Ontario's 407 ETR highway uses a combination of ANPR and radio transponders to toll vehicles entering and exiting the road. Radio antennas are located at each junction and detect the transponders, logging the unique identity of each vehicle in much the same way as the ANPR system does. Without ANPR as a second system it would not be possible to monitor all the traffic. Drivers who opt to rent a transponder for C$2.55 per month are not charged the "Video Toll Charge" of C$3.60 for using the road, with heavy vehicles (those with a gross weight of over 5,000 kg) being required to use one. Using either system, users of the highway are notified of the usage charges by post.
The London congestion charge is an example of a system that charges motorists entering a payment area. Transport for London (TfL) uses ANPR systems and charges motorists a daily fee of £8 paid before 10pm if they enter, leave or move around within the congestion charge zone between 7 a.m. and 6:30 p.m., Monday to Friday. Fines for travelling within the zone without paying the charge are £50 per infraction if paid before the deadline, doubling to £100 per infraction thereafter.
There are currently 1,500 cameras, which use Automatic Number Plate Recognition (ANPR) technology in use. There are also a number of mobile camera units which may be deployed anywhere in the zone.
It is estimated that around 98% of vehicles moving within the zone are caught on camera. The video streams are transmitted to a data centre located in central London where the ANPR software deduces the registration plate of the vehicle. A second data centre provides a backup location for image data.
Both front and back number plates are being captured, on vehicles going both in and out – this gives up to four chances to capture the number plates of a vehicle entering and exiting the zone. This list is then compared with a list of cars whose owners/operators have paid to enter the zone – those that have not paid are fined. The registered owner of such a vehicle is looked up in a database provided by the DVLA. A government investigation has found that a significant portion of the DVLA's database is incorrect. Furthermore, it is now the car owner's responsibility to report to the DVLA if they sell their car.
Up-to-date listing of systems & suppliers exist.
Systems with a simple review step eliminate this argument. Then the machine reports data - date, time, speed measurement and license plate - a good system records a photo of the event - so a person presented with the data is making an accusation. You will get a copy of the data when you go to court.
Similar criticisms have been raised in other countries. Easterbrook also argues that this technology is employed to maximise revenue for the state, rather than to promote safety. The technology is employed to maximize compliance with the laws/regulations of that jurisdiction. It is presumed that the laws/regulations are in place to promote safety and well-being of the citizens.
Older systems had been notably unreliable. This can lead to charges being made incorrectly with the vehicle owner having to pay £10 in order to be issued with proof (or not) of the offense. Improvements in technology have drastically decreased error rates, but false accusations are still frequent enough to be a problem.
Other concerns include the storage of information that could be used to identify people and store details about their driving habits and daily life, contravening the Data Protection Act along with similar legislation (see personally identifiable information). The laws in the UK are strict for any system that uses CCTV footage and can identify individuals.
It is not uncommon to read claims of Automatic Number Plate Recognition read rates in excess of 98%. The experience of system operators is that overall read rates for licence plates are 90% to 94% in ideal conditions with excellent modern systems. In some older systems overall performance rates are rumoured to be between 60% and 80%. ANPR is a developing technology which is coming of age and functional applications are expanding on a steady basis. Although there has been a significant improvement in recent years in the performance of ANPR systems, There has been little on how operators can assess and monitor ANPR/LPR systems.