AI Number Plate Recognition: International Case Studies from 2025

Modern cities worldwide depend on AI number plate recognition technology to manage vehicle movements and combat organized crime, plus coordinate law enforcement responses across jurisdictions. Searching “AI number plate recognition” reveals implementation stories from Brussels to Perth, Wellington to Nashville, demonstrating how AI platforms have become essential urban infrastructure.

The deployments of license plate recognition (LPR) tech share common characteristics. Legacy hardware systems proved inadequate for scaling demands. Standalone cameras created data silos, preventing coordinated responses. But, organizations nowadays need centralized processing capable of handling thousands of cameras while enabling real-time pattern recognition across multiple locations, along with cost savings.

Case Studies: AI Number Plate Recognition Architecture

AI number plate recognition processes video feeds from existing surveillance cameras rather than requiring specialized hardware at each monitoring point. And this approach transforms surveillance infrastructure into intelligent networks coordinating security responses across entire metropolitan regions. Here’s how:

Brussels: Replacing Obsolete Infrastructure with an AI Platform

Brussels demonstrates why organizations transition from legacy hardware to an AI number plate recognition. After terrorist attacks in Paris and Brussels in 2015 and 2016, the Belgian federal government deployed an ANPR camera network for security monitoring.

The system became obsolete within a decade. Police could no longer connect to new cameras. Average speed monitoring failed. Data flows increased significantly, causing stability issues that compromised operational reliability.

The replacement brings together images from all connected cameras, old and new. The architecture supports up to 5,000 cameras initially, with expansion capability to 10,000 units when operational needs require additional coverage.

Brussels police zones will connect 450 cameras first. Additional cameras will be integrated by province or region according to identified security requirements. Complete deployment should finish by 2026 (BelgaNews Agency, 2025).

New Zealand: Exponential Usage Growth Through Private-Sector Integration

New Zealand police demonstrate AI number plate recognition scalability through integration with private-sector camera networks. Usage rose 16% in the past year and 70% over two years, reaching almost 600,000 hits annually, approximately 50,000 monthly queries or 1,600 daily searches.

Thousands of cameras at supermarkets and main roads connect to one of two private systems that over 8,000 police personnel can access. Retailers use automated number plate recognition camera footage to back up crime reports, while police tap into the system for law enforcement applications spanning investigations, stolen vehicle recovery, and organized crime tracking (RNZ, 2025).

Integration across public and private sectors would prove impossible with hardware-embedded systems requiring direct police ownership of specialized equipment. Server-based platforms enable shared infrastructure where multiple organizations access centralized processing through secure interfaces.

Western Australia: Retail Crime Prevention Through Surveillance Escalation

Western Australian police and Westfield owner Scentre Group deployed AI number plate recognition technology at Perth’s Westfield Carousel to track known and suspected criminals entering and leaving shopping centers. This represents a substantial escalation of surveillance measures addressing retail crime.

WA Police’s automatic number plate recognition cameras monitor vehicle movements at shopping center access points, cross-referencing plates against criminal databases and watchlists. When vehicles associated with known offenders enter parking facilities, alerts notify security personnel plus law enforcement for proactive intervention (Financial Review, 2025).

The deployment demonstrates AI number plate recognition applications extending past traffic management into crime prevention. Shopping centers face organized retail theft operations where criminal groups target multiple locations systematically. Traditional security measures react after thefts occur, whereas vehicle recognition enables predictive responses before criminals enter facilities.

Server-based architecture proves essential for this application, as criminals operate across multiple shopping centers. Requiring recognition systems that coordinate alerts across distributed locations. When suspect vehicles appear at Carousel today after hitting other locations yesterday, pattern recognition capabilities identify habitual offenders warranting enhanced surveillance.

Also you can learn more about: Automatic number plate detection

AvidBeam’s AvidAuto: License Plate Recognition (LPR) Platform

AvidBeam Technologies delivers AI number plate recognition through AvidAuto. An AI video analytics platform built on ATUN, AvidBeam‘s scalable analytics framework. The solution translates vehicle data into operational intelligence supporting security, parking management, and traffic coordination applications.

AvidAuto transforms existing surveillance cameras into intelligent recognition systems without requiring specialized hardware replacements.

AvidAuto’s Capabilities

• Arabic license plate recognition: 98%+ accuracy.
• English license plate recognition: 92%+ accuracy.
• Day/night operation maintaining consistent performance.
• Character recognition across diverse plate formats and conditions.
• Vehicle color, make, and model identification.

• License plate number identification with watchlist verification.
• Create vehicle allow/deny lists for facility management

AvidAuto’s Forensic Search Functions

• Track specific vehicles by license plate number across locations.
• Vehicle count reporting with daily/weekly/monthly aggregation.
• Monitor total entrances by multiple criteria.
• Graphical data representation through customizable dashboards.

AvidAuto and Scalability Demonstration

1. For Video Analytics at Qiddiya, Saudi Arabia (2024), AvidAutoTM monitors vehicle count. Performs license plate recognition (LPR), manages whitelist and blacklist vehicles, supports vehicle searches, and controls access at entrances, enhancing security and operational efficiency.
2. In the Sawaher Project, KSA (2024), AvidAutoTM detects and tracks vehicles via LPR. Identifies watchlist, whitelist, and deny list vehicles, and monitors vehicle flow and counting to improve traffic efficiency.
3. For Mostakbal City, Egypt (2023), AvidAutoTM is deployed to monitor vehicle count, perform LPR. Manage whitelist and blacklist, enable vehicle searches, and control entrance access points.
4. At Tower 007 in New Alamein City, Egypt (2022), AvidAutoTM monitors vehicle count. Conducts LPR with whitelist and blacklist management, facilitates vehicle searches, and integrates LPR with access control systems.
5. At Four Seasons Hotel, Sharm El Sheikh (2022), AvidAutoTM monitors vehicle count, performs license plate recognition (LPR). Manages whitelist and blacklist vehicles, supports vehicle searches, and controls access at entrances to ensure efficient vehicle management and security.
6. In the Smart Communities project at Madinaty (2022), AvidAutoTM was deployed in a multi-lane setup to monitor vehicle count. Execute LPR, manage whitelist and blacklist, and search for vehicles across all entrances and exits of Madinaty, providing comprehensive traffic and access control.

Also you can learn more about: License plate recognition

AI Number Plate Recognition and Generative AI Integration

AvidBeam continues advancing AI number plate recognition technology through generative AI integration, currently under development. This enhancement will enable conversational interfaces where users query systems using natural language rather than structured database searches.

1. Operators could ask: “Show vehicles matching the stolen Nissan description entering downtown between 2-4 PM yesterday” rather than constructing complex search parameters. The system would interpret intent, execute appropriate queries, and present results with contextual explanations.
2. During investigations, analysts could request: “Track this license plate’s movements over the past week and identify other vehicles frequently appearing at the same locations.” The generative AI would recognize the request seeks pattern analysis, execute temporal correlation queries, and generate reports highlighting potentially related vehicles.

Also you can learn more about: LPR camera

All in All

AI number plate recognition has evolved from a specialized law enforcement tool into an essential urban infrastructure supporting security and operational efficiency across public/private sectors. The technology shift from hardware-embedded systems to server-based number plate recognition platforms reflects organizational recognition that centralized processing delivers superior scalability and coordination capabilities.

Deployments across Brussels, New Zealand, and Western Australia demonstrate centralized architecture advantages through implementations spanning thousands of cameras and maintaining operational reliability and recognition accuracy.

AvidBeam‘s AvidAuto platform exemplifies this technological evolution through solutions that transform existing surveillance infrastructure into intelligent recognition networks.