AI Box for Auto License Plate Recognition (ALPR) Accelerated Application
by: Uncanny Vision
AI Box for ALPR/NPR is a deep learning application that decodes multiple real-time camera streams, detects, tracks vehicles and reads the text of license plates. Common applications include smart parking, gate security, traffic management, and law enforcement.
AMD Partner Video: Uncanny Insighter - Gate ANPR/LPR
Features:
- Video pipeline with multiple AI models for detection of vehicles, license plates, vehicle classification, and license plate recognition
- Up to 2 streams of H.264/H.265 decode at 1080p resolutions
- End-to-End LPR solution including dashboards, remote monitoring, blacklist and whitelist of vehicles
- Open REST APIs for custom integration
- Complete application with including hardware design
Hardware Needed:
Additional Tools and Resources
Frequently Asked Questions
No, the app does not need any experience in FPGA design.
Off the shelf LPR for North America, Europe, India, Australia. Customization options for other countries available on request.
Yes
Both monthly subscription and one time license options are available.
No, LPR solution is fully video, and camera based.
Featured Documents
Powering Electric Drive Control & Sustainability with Adaptive Computing
Kria™ adaptive System-on-Module (SOM) devices from AMD play an important role in electric drive control. They can optimize performance, help a motor run more efficiently, reduce power consumption, mitigate noise, cut vibration, and detect potential failures before they happen. Download our new motor control eBook to learn more!
Accelerate Your AI-Enabled Edge Solution with Adaptive Computing
Learn all about adaptive SOMs, including examples of why and how they can be deployed in next-generation edge applications, and how smart vision providers benefit from the performance, flexibility, and rapid development that can only be achieved by an adaptive SOM.
Adaptive Computing in Robotics
Demand for robotics is accelerating rapidly. Building a robot that is designed to be safe and secure and can operate alongside humans is difficult enough. But getting these technologies working together can be even more challenging. Complicating matters is the addition of machine learning and artificial intelligence, which is making it more difficult to keep up with computational demands.
Roboticists are turning toward adaptive computing platforms, which offer lower latency and deterministic, multi-axis control with built-in safety and security features on an integrated, adaptable platform that is expandable for the future. Read the eBook to learn more.