Demo | Intelligent Queue Management with YOLOv8 Object Detection & Counting Edge AI Reference Kit
6An in-depth exploration of the code that makes our Edge AI Reference Kit’s Intelligent Queue Management application work. Our ready-made kits come with pre-built components and pretrained models to accelerate your AI solution development.
Explore the inner workings behind each module that powers our application, and walk through how it runs step-by-step with our Jupyter Notebook. Follow us through installing the requirements for this application, converting and optimizing your models, optimizing them through the NNCF post-training quantization training API, and running the application itself. After exploring and understanding the intricacies of this code, you will be equipped to build on top of this code for your own custom solutions.
OpenVINO is an open source toolkit that accelerates AI inference on a wide variety of hardware, including CPUs and GPUs, and powers our Edge AI Reference Kits.
The kit in this video focuses on Intelligent Queue Management, an AI-based application that enables cameras to detect and count the number of customers in a queue. This is possible through computer vision and object detection, which expedites the time customers spend in line, getting them from start to finish, faster. You’ll also have access to real-time customer insights and can quickly correct any issues that may arise during the checkout process. Be alerted to take actions like rerouting customers, opening new lines, or adjusting your staffing on the floor.
Open Potential (Real-World AI Solutions): https://intel.ly/3YG3Q4t
Get the code at the OpenVINO Github with Jupyter Notebook: https://bit.ly/3DVDBNz
Learn more about the OpenVINO toolkit: https://bit.ly/3DVDBNz
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Demo | Intelligent Queue Management with YOLOv8 Object Detection & Counting Edge AI Reference Kit