With the AutoVu SharpZ3, vehicle-based ALPR technology is about to enter a new phase in its evolution, made possible by Intel’s high-performance, low-power, deep learning VPU
MONTREAL (PRWEB)
October 22, 2019
Genetec at IACP 2019
Booth # 5218
Chicago October 26-29, 2019
Genetec Inc. (“Genetec”), a leading technology provider of unified security, public safety, operations, and business intelligence will unveil its next-generation mobile ALPR (automatic license plate recognition) system at IACP (Chicago, October 26-29 2019) on booth #5218. Powered by Intel® Movidius™ Myriad™ X VPU (Vision processing unit), the new Genetec AutoVu™ SharpZ3 is among the first specialized in-vehicle ALPR systems in the world to use Intel’s latest deep learning and computer vision technology. The deep learning capabilities in the AutoVu SharpZ3 system will enable advanced levels of innovation in in-vehicle analytics, situational awareness, and accuracy.
“With the AutoVu SharpZ3, vehicle-based ALPR technology is about to enter a new phase in its evolution, made possible by Intel’s high-performance, low-power, deep learning VPU. By bringing powerful deep learning processing into the device itself, we are no longer limited by the constraints of traditional computer vision techniques or server post processing. The new AutoVu SharpZ3 system will enable cities and law enforcement organizations to go far beyond simple license plate character recognition,” said Stephan Kaiser, General Manager of AutoVu at Genetec.
The AutoVu SharpZ3 system will not only be able to improve the accuracy of license plate reads in difficult environments (such as bad weather, heavy traffic, and fast speeds) but will also be able to instantly recognize additional analytics such as, vehicle type, color, and more.
With future releases, the deep learning capabilities in the AutoVu SharpZ3 will enable cities to use their ALPR equipped vehicles to address other operational issues such as detecting unpermitted road construction, discovering abandoned e-scooters or bikes in unauthorized zones, and more.
“AI is driving a powerful digital transformation in cities around the world – enabling them to be more efficient, livable and secure for their citizens. By pushing AI-powered devices and solutions to the edge, forward-thinking companies like Genetec are fulfilling the true promise of IoT in smart cities,” said Jonathan Ballon, Intel vice president and general manager, Internet of Things Group.
The AutoVu SharpZ3 system also features three optical sensors in the camera to provide additional analytics such as the precise positioning of detected objects, and a modular chassis to allow users to easily add new functionalities in the field without the complication and costs of hardware replacement.
The new Genetec AutoVu SharpZ3 system is expected to become available by early 2020. For more information, please visit the Genetec booth #5218 at IACP or visit http://www.genetec.com/SharpZ3.
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About Genetec
Genetec Inc. is an innovative technology company with a broad solutions portfolio that encompasses security, intelligence, and operations. The company’s flagship product, Security Center, is an open-architecture platform that unifies IP-based video surveillance, access control, automatic license plate recognition (ANPR), communications, and analytics. Genetec also develops cloud-based solutions and services designed to improve security, and contribute new levels of operational intelligence for governments, enterprises, transport, and the communities in which we live. Founded in 1997, and headquartered in Montreal, Canada, Genetec serves its global customers via an extensive network of resellers, integrators, certified channel partners, and consultants in over 80 countries.
For more information about Genetec, visit: http://www.genetec.com
© Genetec Inc., 2019. Genetec, AutoVu, SharpZ3, and the Genetec logo are trademarks of Genetec Inc. and may be registered or pending registration in several jurisdictions. Other trademarks used in this document may be trademarks of the manufacturers or vendors of the respective product.
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