Yolo Pose Estimation and Skeleton

Introduction

Make a pose estimator and use it to make a moving skeleton.

Use Yolo from Ultralytics.

• Python 3.7+
• Yolo v11
• A CUDA-enabled GPU (optional but recommended for faster inference).

pip install ultralytics opencv-python numpy

Yolo

There are 17 keypoints. YOLOv11’s pose model outputs:

• (x, y) coordinates for each keypoint and
• confidence scores indicating the model’s certainty in each keypoint’s position.

Image detection

• 

  AN image before and after the code.

from ultralytics import YOLO
import matplotlib.pyplot as plt
import cv2
from PIL import Image

model = YOLO("yolo11n-pose.pt")  # n, s, m, l, x versions available

results = model.predict(source="sample_image.jpg")  

plt.figure(figsize=(10, 10))
plt.title('YOLOv11 Pose Results')
plt.axis('off')
plt.imshow(cv2.cvtColor(results[0].plot(), cv2.COLOR_BGR2RGB))

References

• https://medium.com/@staytechrich/human-pose-estimation-with-yolov11-96932a5d7159
• https://www.labellerr.com/blog/how-to-perform-yolos-various-task/