FLOCK automatic number plate recognition
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==== In mobile systems ====
During the 1990s, significant advances in technology took automatic number-plate recognition (ANPR) systems from limited expensive, hard to set up, fixed based applications to simple "point and shoot" mobile ones. This was made possible by the creation of software that ran on cheaper PC based, non-specialist hardware that also no longer needed to be given the pre-defined angles, direction, size and speed in which the plates would be passing the camera's field of view. Further scaled-down components at lower price points led to a record number of deployments by law enforcement agencies globally. Smaller cameras with the ability to read license plates at higher speeds, along with smaller, more durable processors that fit in the trunks of police vehicles, allowed 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 160 km/h (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 electrical system, and equipment must have minimal space requirements.
Relative speed is only one issue that affects the camera's ability to 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 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 oncoming traffic. In this case one camera may be turned backwards.
=== Algorithms ===
There are seven primary algorithms that the software requires for identifying a license plate:
Plate localization – responsible for finding and isolating the plate on the picture
Plate orientation and sizing – compensates for the skew of the plate and adjusts the dimensions to the required size
Normalization – adjusts the brightness and contrast of the image
Character segmentation – finds the individual characters on the plates
Optical character recognition
Syntactical/Geometrical analysis – check characters and positions against country-specific rules
The averaging of the recognised value over multiple fields/images to produce a more reliable or confident result, especially given that any single image may contain a reflected light flare, be partially obscured, or possess other obfuscating effects.
The complexity of each of these subsections of the program determines the accuracy of the system. During the third phase (normalization), 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.
Contemporary ANPR systems use multiple data sources and analytical techniques that go beyond simple number plate recognition. Weigh-in-Motion uses ANPR cameras and AI analytical techniques to calculate the weight of a vehicle and to alert if the weight is too high for the vehicle and conditions.
==== Difficulties ====
There are a number of possible difficulties that the software must be able to cope with. These include:
Poor file 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
Read license plates that are different at the front and the back because of towed trailers, campers, etc.
Vehicle lane change in the camera's angle of view during license plate reading
A different font, popular for vanity plates (some countries do not allow such plates, eliminating the problem)