Multi-View 3D Human Pose Estimation for Risk Behavior Detection

With the continuous advancement of artificial intelligence in the field of computer vision, dangerous behavior monitoring based on visual information has garnered increasing attention from researchers compared to traditional sensor-based methods. However, flat images captured by a single camera are easily affected by occlusion and lack depth information. This article introduces a dangerous behavior monitoring system leveraging multi-view 3D human pose estimation, and enhances its effectiveness in crowded scenes by integrating bottom-up and top-down approaches. Our proposed method demonstrates significant improvements in accuracy and practicality over traditional techniques, making it highly promising for real-world applications.