The AI Box with ReID accelerated application performs distributed, scalable, multi-stream tracking and Re-Identification. The app leverages machine learning for pedestrian tracking and decoding multiple camera streams and performs pedestrian detection and tracking across camera feeds. Common applications include smart cities, retail analytics, and video analytics.
No, the app does not require any experience in FPGA design.
This app is free of charge from AMD.
AMD has tested a specific set of cameras that support H.265/H.264 RTSP streams. However, the app is expected to work with any H.264/H.265 encoded streams.
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!
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.
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.