Unstructured Data Processing from Cloud to Edge

In this tutorial, we will create a real-time pose estimation system using Raspberry Pi and Milvus, an open-source vector database. The system leverages edge AI processing capabilities of the Raspberry Pi to perform object detection and pose estimation on live video streams. It utilizes a YOLOv8 model for object detection and a Hailo AI accelerator for efficient inference. The processed data is then stored in Milvus, allowing for fast and accurate similarity searches. We will also demonstrate how to integrate the system with Slack for real-time notifications and updates. This tutorial assumes that you have basic knowledge of Python programming and GStreamer, a framework for building multimedia applications. Here is an overview of the steps we will follow: 1. Set up the environment and install required dependencies. 2. Create a YOLOv8 model for pose estimation. 3. Implement a callback function to process video frames using Hailo SDK. 4. Create a utility function for COCO keypoints. 5. Create the GStreamer pipeline. 6. Execute the program and observe the results. 7. Explore use cases of combining AI and vector databases. By the end of this tutorial, you will have built a real-time pose estimation system that can be easily adapted for various applications in robotics, smart cities, industrial automation, healthcare, and more.