Data shows successful gathering of new identifying characteristics that strengthen worldwide ALPR offering and show potential to save thousands.

Q-Free (OSE: QFR), a global leader in mobility solutions for smart city infrastructure, today announced the company is developing improved vehicle analytics and detection for its automatic license plate recognition (ALPR) technology that identifies vehicle class, color, make/model (MMR), and which side of the vehicle is being analyzed.

The vehicle analytics feature is an extension of the company’s Intrada® ALPR which processes more than a billion license plates around the globe each day. As a vendor-agnostic solution, Q-Free sees this as a convenient extension that opens new possibilities for its customers’ operations and business models without the need to change their existing video infrastructure or invest in costly hardware-based alternatives such as radar and laser.

Data from test sites in South America and Asia show surveillance and security operators successfully gathering additional identifying characteristics leveraging existing video detection equipment.

• Vehicle Class – Enables operators to charge differently for motorcycles, cars, and buses.
• Vehicle Color – Helps operators and law enforcement easily locate matching vehicles.
• Vehicle Model/Make – Further helps agencies and officers identify lost or suspect vehicles.
• Vehicle Angle – Determines which side of the vehicle is facing the camera, e.g. front of rear, helpful in determining entry and exit points in parking applications.

The new vehicle analytics are a result of innovative, reliable neural networks and the company’s machine learning capabilities according to Marco Sinnema, product manager for Q-Free’s Intrada ALPR library. “Work with initial customers continues to train the detection of the neural networks – which is now available in our commercial, off-the-shelf Intrada ALPR library. Early results are showing the system performing with great precision, and we plan on delivering the same unrivalled automation accuracy and low error rates offered in our existing ALPR solutions.”