Data enrichment and contextual data is used to enhance raw information and broaden or deepen the narrow context of original data. It is used in all sectors, throughout all industries including the security and mobility industry. In combination with license plate recognition it is used for law enforcement, in access control solutions and to enrich loyalty systems.
Look beyond a license plate
Market demands involve the refinement of alerts and alarms and the upgrade for access allowance, to meaningful and intelligent bits of information for alarm and control centralists. To look beyond a license plate, our ANPR results are enhanced with third party data and contextual information. Deeper and more detailed knowledge leads to more informed decisions and is the base for a set of predefined rules, triggers and actions, to execute desired functionality.
Internal and external sources for enrichment
In computer vision systems, the distinction can be made between internal and external enrichments. Internal enrichments originate from the system itself by the visual interpretation of an image or video. Direction, speed of travel or origin of license plate are clear examples of internal processes. External enrichments find their origin in third party databases. They are the source of information about stolen vehicles, or if they are allowed to get access to a certain area.
Data enrichment
Data enrichment is defined as merging third-party data from an external authoritative source with an existing database of first-party customer data. Integration of third party sources with our ANPR solution resulted in specialized applications, also not being possible before.
1. Vehicle registration authorities
All countries have a vehicle registration authority and in many countries governments made vehicle data publicly available. Recognized license plates are linked to corresponding make, model, year of build and color by a database lookup. This data is greatly informative and, important in regards to privacy, anonymous.
Fuel theft is a recurring problem at petrol stations and storage locations. The implementation of car data enables users of DPS, our Drive-off Prevention System, to recognize false or suspected license plates and stop fuel theft even before it occurs.
Carwash entrepreneurs use ANPR with car data to enhance their loyalty systems or enrich their customer databases and marketing intelligence. It builds a more complete understanding of their costumers, recurring visits and the effectiveness of advertisements and promotions. It is a great tool to clarify business goals and measure the results.
2. Third party data
A good example of the added value of third party data in combination with license plate reads, is the VbV (Verzekeringsbureau Voertuigcriminaliteit) integration with our AI platform in the Netherlands. VbV is a collective of car insurance companies, that keeps a database of stolen or suspected vehicles with the objective to fight vehicle crime and reduce financial, personal and emotional damage for insured parties. The database is available for certified security companies or alarm centers in their effort to increase security. The integration with VbV, triggers relevant alerts in an alarm control center. It provides a centralist with a true alarm to follow up, making security services or enforcement far more efficient and responsive than it ever was.
Contextual data
Contextual data is information that provides context to an event, person, or item. This data provides a broader understanding of specific pieces of information and places them in a larger picture. The integration of contextual data with license plate recognition has a more generic character. It is broadly applicable in many solutions and eases the process of finding certain transactions or data from the past.
1. Direction of travel
The direction of travel module, part of our integration platform, detects if a vehicle is moving towards or away from a LPR camera. It differentiates between vehicles entering or leaving a location. Alerts can be transmitted when a vehicle, going against normal vehicle flow, is detected. In access control applications it provides a solution for parking areas with a combined entry and exit road. The ANPR system will only open the gate for incoming vehicles, or could count the amount of vehicles present on or in the parking.
2. Vehicle classification
The distinction between trucks, motors, cars, bicycles and pedestrians on top of ANPR enables refined access or crowd management control. A truck can be granted access to the perimeter of a logistic center, while an arriving car will not be allowed. Meanwhile the staff receives a message to announce the arrival of the truck and the details of the load. A matrix board leads the truck driver to a specified docking station.
The ability to detect people, vehicles and other objects, while reading license plates provides a complete overview of traffic flow in a tunnel, on a bridge, city landscape or business park.
Data enrichment and contextual data on top of LPR results equip our all-in-one camera systems with all the on board functionality, to offer value added applications. It makes our ANPR camera smart and deeply appreciated in projects, having specific requirements and complexity.
