Answer to: How to analyze classroom behavior using computer vision and pose estimation?

One possible approach is to combine object detection, pose estimation and behavior analysis. A typical pipeline could look like this: 1. Use an object detection model such as YOLOv8 to detect students in the classroom. 2. Apply pose estimation (for example MediaPipe Pose or OpenPose) to extract skeleton keypoints. 3. Analyze interactions using spatial distance or temporal patterns. 4. Aggregate the results into behavioral indicators such as attention level, activity level and social interaction. I implemented a prototype system using this idea in an open-source project: https://github.com/KEYUJIN881129/AI-Based-Five-Capability-Smart-Kindergarten-System The project demonstrates how computer vision and multimodal AI can be used to estimate behavioral indicators such as attention, participation and activity level in a classroom environment. It may provide a useful reference for building a similar system.