Automatic number plate recognition

The system must be able to deal with different styles of licence plates

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. 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 the day. A powerful flash is included in at least one version of the intersection-monitoring cameras, serving both to illuminate the picture and to 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.

ANPR is sometimes known by various other terms:

• Automatic licence plate recognition (ALPR)
• Automatic vehicle identification (AVI)
• Car plate recognition (CPR)
• Licence plate recognition (LPR)
• Lecture Automatique de Plaques d'Immatriculation (LAPI)

Development history

The ANPR was invented in 1976 at the Police Scientific Development Branch in the UK. Prototype systems were working by 1979, and contracts were let to produce industrial systems, first at EMI Electronics, and then at Computer Recognition Systems (CRS) in Wokingham, UK. Early trial systems were deployed on the A1 road and at the Dartford Tunnel. The first arrest due to a detected stolen car was made in 1981.

Components

The software aspect of the system runs on standard PC hardware and can be linked to other applications or databases. It first uses a series of image manipulation techniques to detect, normalise and enhance the image of the number plate, and then optical character recognition (OCR) to extract the alphanumerics of the licence plate. ANPR systems are generally deployed in one of two basic approaches: one allows for the entire process to be performed at the lane location in real-time, and the other transmits all the images from many lanes to a remote computer location and performs the OCR process there at some later point in time. When done at the lane site, the information captured of the plate alphanumeric, date-time, lane identification, and any other information that is required is completed in somewhere around 250 milliseconds. This information, now small data packets, can easily be transmitted to some remote computer for further processing if necessary, or stored at the lane for later retrieval. In the other arrangement, there are typically large numbers of PCs used in a server farm to handle high workloads, such as those found in the London congestion charge project. Often in such systems, there is a requirement to forward images to the remote server, and this can require larger bandwidth transmission media.

Technology

The font on Dutch plates was changed to improve plate recognition.

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.

ANPR in mobile systems

Recent advances in technology have taken automatic number plate recognition (ANPR) systems from fixed applications to mobile ones. Scaled-down components at more cost-effective price points have led to a record number of deployments by law enforcement agencies around the world. Smaller cameras with the ability to read license plates at high speeds, along with smaller, more durable processors that fit in the trunks of police vehicles, allow law enforcement officers to patrol daily with the benefit of license plate reading in real time, when they can interdict immediately.

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 (160 km/h), 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. 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 ineffective with incoming traffic. In this case one camera may be turned backwards.

Algorithms

Steps 2, 3 and 4: The licence plate is normalised for brightness and contrast, and then the characters are segmented to be ready for OCR.

There are six primary algorithms that the software requires for identifying a licence plate:

1. Plate localisation – responsible for finding and isolating the plate on the picture.
2. Plate orientation and sizing – compensates for the skew of the plate and adjusts the dimensions to the required size.
3. Normalisation – adjusts the brightness and contrast of the image.
4. Character segmentation – finds the individual characters on the plates.
5. Optical character recognition.
6. Syntactical/Geometrical analysis – check characters and positions against country-specific rules.

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.

Difficulties

Early ANPR systems were unable to read white or silver lettering on black background, as permitted on UK vehicles built prior to 1973.

There are a number of possible difficulties that the software must be able to cope with. These include:

• Poor image resolution, usually because the plate is too far away but sometimes resulting from the use of a low-quality camera.
• Blurry images, particularly motion blur.
• Poor lighting and low contrast due to overexposure, reflection or shadows.
• An object obscuring (part of) the plate, quite often a tow bar, or dirt on the plate.
• (some countries do not allow such plates, eliminating the problem).
• Circumvention techniques.
• Lack of coordination between countries or states. Two cars from different countries or states can have the same number but different design of the plate.

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, tow bars may obscure one or two characters of the licence plate. Bikes with 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 barricaded 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.

Imaging Hardware

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.

Blurry images make OCR difficult or impossible. ANPR systems should have fast shutter speeds to avoid motion blur.

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 travelling at 120 mph (190 km/h).

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

Circumvention techniques

Vehicle owners have used a variety of techniques in an attempt to evade ANPR systems and road-rule enforcement cameras in general. One method increases the reflective properties of the lettering and makes it more likely that the system will be unable to locate the plate or produce a high enough level of contrast to be able to read it. This is typically done by using a plate cover or a spray, though claims regarding the effectiveness of the latter are disputed. In most jurisdictions, the covers are illegal and covered under existing laws, while in most countries there is no law to disallow the use of the sprays. Other users have attempted to smear their license plate with dirt or utilize covers to mask the plate.

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 alpha-numerics.

In order to avoid surveillance or penalty charges, there has been an upsurge in car cloning. 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 behaviour 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, but it would need to almost visibly glow red to work.

Police enforcement

Closed-circuit television cameras such as these can be used to take the images scanned by automatic number plate recognition systems.

The UK has an extensive (ANPR) automatic number plate recognition CCTV network. Effectively, the police and Security services track all car movements around the country and are able to track any car in close to real time. Vehicle movements are stored for 5 years in the National ANPR Data Centre to be analyzed for intelligence and to be used as evidence.

In 1997 a system of one hundred ANPR cameras, codenamed GLUTTON, was installed to feed into the automated British Military Intelligence Systems in Northern Ireland.  Further cameras were also installed on the British mainland, including unspecified ports on the east and west coasts.

Average Speed cameras

ANPR is for speed limit enforcement in the UK 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 longest stretch of average speed cameras in the UK is found on the A77 road in Scotland, with 30 miles (48 km) being monitored between Glasgow and Ayr. In 2006 it was confirmed that speeding tickets could potentially be avoided from the 'SPECS' cameras by changing lanes and the RAC Foundation feared that people may play "Russian Roulette" changing from one lane to another to lessen their odds of being caught, however in 2007 the system was upgraded for multi-lane use and in 2008 the manufacturer described the "myth" as “categorically untrue”. There is no evidence that average speed cameras actually reduce accident rates long term, with many motorists arguing that average speed check systems encourage bunching.

Source: Wikipedia

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