Automated Number Plate Recognition (ANPR) System
ANPR/ALPR (Automated Number Plate / License Plate) cameras today utilize advanced technologies which do not require the use of infrared LEDs, as found on this unit above. Since Automated License Plate Reader (ALPR) systems first gained recognition as "force multipliers" for Law Enforcement, they have enabled significant improvements in everything from stolen vehicle recoveries to criminal intelligence gathering.
ALPR (Or ANPR): Where It's Been And Where It's Going
An ALPR system's core components remain the same now as when it was first invented: high-speed cameras; Optical Character Recognition (OCR); a way to compare the characters rendered from OCR to databases or "hot lists" of license plates of interest; and an interface which alerts police officers when there's a "hit" on a hot list.
S3-ALPR Is Saving Costs With Commercial Hardware
How those elements come together has evolved over the years. For example, no longer is infrared a strict requirement. The advent of the megapixel IP camera using white LED light allows newer LPR systems to use color and high definition without need infrared. Users can also obtain more forensic details; not only the tag itself, but also the vehicle's make, model and any identifying marks. Such high-definition cameras are no longer limited to proprietary hardware. Now, they're available off-the-shelf in commercial models. In addition to better detail, they allow plate capture from a wider field of view - for example, all lanes of a superhighway - longer distances and different angles, as well as the capture of multiple plates from one image. With our system, even mobile device cameras can be used for ALPR. Mobile applications offer value in situations such as the need for vehicle info during response to a major incident. In these and other situations, ALPR removes challenges associated with hand-written notes, including illegible writing and transposed characters. In turn, ALPR adds the value of metadata - the date, time, location, and any additional images of associated vehicles. Our software drives down costs from the tens of thousands into the thousands - or even less. S3-ALPR software features such as high accuracy number plate origin details, H.264 advanced video coding support, plate grouping, high-speed processing, and access via a Web apps. Our ALPR technology is used instantly to scan for the fugitives' plates in a stake-out and alert agents when they get a hit.
Multiple Data Storage Options
Agencies which cannot afford a large-scale cloud storage solution can opt for an available "hybrid" service. Agencies don't need to rely on third parties for storage, however. Many have fed a demand for cloud-based storage to maintain not just ALPR data, but all digital evidence collected from in-car and body-worn camera systems, as well as other systems. As a result, our ALPR systems integrates with cloud solution providers, such as Microsoft "Azure" Government or Amazon Web Services (AWS), both of which implement best practices to meet standards which can be considered compliant with — either meeting or exceeding — Criminal Justice Information Services (CJIS) requirements and others, such as the International Organization for Standardization (ISO). S3-ALPR's Cloud Stream solution in which an agent running on a local server or laptop sends metadata to the cloud for displaying results. The processed image data is then retained locally, depending on the agency's storage capacity and retention requirements.
From Collection To Analytics
The ability to filter ever increasing data sets is of growing importance to law enforcement. By using our filtering capability by vehicle year, make and model. Taking all scans in a quarter mile radius over a 24 hour period might yield three thousand vehicles. By filtering that list through an eyewitness description of a suspect vehicle, you can reduce that list to maybe a dozen leads as a starting point. Much of ALPR's resurgence comes from "the Big Data movement," and machine learning that makes ALPR more accurate overall. You can feed the system with tens of thousands of vehicle images and data points, so that it can learn what, for instance, Toyota Camrys look like. Then, as it processes new images, it can find suspect vehicles faster than with license plate characters alone. This relieves witnesses of the responsibility of having to recall full plate numbers and other details.
Putting It All Together
Policy mixed together with good strategy and training is well worth the investment, considering the advancements in ALPR which stand to improve its force multiplying attributes. Many agencies, for example, collect, but then do nothing with the data. This may be because their ALPR "pings" on too many hot lists, overwhelming individual officers who cannot handle all the alerts to handle expired plates, lack of inspection stickers and other minor offenses - even when they get mixed in with alerts for active warrants or vehicles connected to crimes. You have to be able to link the data you collect to a business objective. This should be based on empirical research showing not just the number of hits to a hot list, but also how many of those were actionable and over what period of time they remain actionable. We help Law Enforcement agencies develop ALPR metrics, which are recommended as "universal" include proportion of captures, read accuracy and matching efficiency, as well as the number of vehicle recoveries. Measuring these numbers over time can help chart a course for evaluation during the procurement process, either for renewal or when evaluating new technology from a different vendor. S3-ALPR Software being used on a Off-the-Shelf Camera for Plate numbers recognition on a public street. We believe that ALPR/ANPR technology should be able to support Law Enforcement objectives, including, for example, database technology which can allow for scheduling an automated dump of irrelevant data on an interval defined by the agency's goals, strategy and policy. ALPR has made many strides over its 30 year lifespan and is expected to make many more in the future. By maintaining an ongoing dialogue with research, vendors and peers, we continue to improve our technology and it continues to shape and be shaped as an ongoing process.