SecurityWorldMarket

19/09/2020

Genetec ALPR tests show accuracy affects bottom line

Montreal, Canada

While the accuracy rate of traditional rules-based ALPR systems is very good, every false positive still requires human intervention. These service interruptions not only cause customer dissatisfaction but also cost parking operators time, money and create overall enforcement inefficiencies. 

To address these issues, Genetec has developed a next-generation machine learning-based engine for Security Center Autovu. Optimised to run on current ALPR hardware already used by customers in the field, Genetec Autovu MLC improves plate-read performance by an additional 5%. To help parking organisations quantify the impact this increase in plate reading accuracy can have on their bottom line, Genetec has developed an ALPR calculator.

By simply entering the typical citation amount, the number of days of enforcement per week, the hours of enforcement per day, and the violation rate percentage, parking organisations can use the calculator to discover they can save thousands of dollars a year.

"While a 5% increase in plate reading performance may not seem like much initially, it can have a profound effect on a parking operator's bottom line," said Michael Bradner, Product Manager at Genetec. "Shift after shift, day after day, and month after month, that cumulative effect adds up to tens of thousands of dollars a year. With the ALPR calculator, customers can clearly see for themselves."

The Autovu MLC is available as a firmware update for new and existing Autovu Sharp VTM hardware from Genetec certified channel partners and integrators. It does not require costly hardware upgrades and can run on Autovu Sharp V and most Sharp X cameras already deployed in the field. Autovu MLC is embedded in the new Genetec Sharp Z3, one of the first specialised in-vehicle ALPR systems in the world to be powered by Intel's latest machine learning and computer vision technology. This brings high-performance data processing and analytics to the edge to enhance deep learning capabilities that extend beyond traditional ALPR.


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