The YOLO-Pose framework is a novel approach designed for multi-person pose estimation, leveraging the strengths of the YOLO (You Only Look Once) object detection framework. Here’s an overview of its approach and architecture:

• Base Architecture: YOLO-Pose builds upon YOLOv5, which is recognized for its balance of accuracy and complexity in detecting objects. This choice allows the model to effectively detect humans, which is crucial for pose estimation .
• Keypoint Detection: The model focuses on a single class detection problem, specifically for persons, where each individual is associated with 17 keypoints. Each keypoint is represented by its location and confidence score, leading to a total of 51 elements predicted for each anchor .
• Prediction Heads: YOLO-Pose employs a dual-head architecture:
  • Box Head: This head predicts the bounding boxes for detected persons.
  • Keypoint Head: This head predicts the keypoints associated with each detected person. For each anchor, the keypoint head outputs 51 elements, while the box head outputs six elements .
• Training Methodology: The model is trained using Object Keypoint Similarity (OKS) loss, which allows for end-to-end training. This is a significant improvement over traditional heatmap-based methods that rely on surrogate losses and are not end-to-end trainable .
• Feature Fusion: YOLO-Pose utilizes CSP-darknet53 as its backbone and PANet for fusing features from various scales. This multi-scale feature fusion is essential for accurately detecting keypoints at different resolutions .
• Inference Process: During inference, the model retains keypoints with a confidence score greater than 0.5, filtering out those that are outside the field of view. This helps prevent the issue of dangling keypoints, which can lead to deformed skeletons .
• Performance: YOLO-Pose achieves competitive results on the COCO keypoint dataset, demonstrating state-of-the-art performance with an AP50 of 90.2% on validation and 90.3% on the test-dev set, all without the need for test-time augmentations like flip tests or multi-scale testing .

In summary, YOLO-Pose represents a significant advancement in pose estimation by integrating object detection and keypoint prediction into a single, efficient framework, thereby addressing the limitations of both top-down and bottom-up approaches.