🧘 Pose Estimation Simulator

Track human body movement with keypoint detection and skeleton tracking

Your Progress

0 / 5 completed
Previous Module
Face Recognition Pipeline

Introduction to Pose Estimation

🎯 What is Pose Estimation?

Pose estimation is a computer vision technique that detects and tracks human body keypoints in images or videos. It identifies joint positions (shoulders, elbows, knees, etc.) and creates a skeleton representation of the human body for motion analysis and tracking.

💡
Key Concept

Modern pose estimation models can detect 17+ keypoints in real-time, enabling applications from fitness tracking to motion capture for games.

🏃
Sports Analytics

Analyze athlete movements, improve form, and prevent injuries

🎮
Gaming & AR

Motion capture for games, virtual try-ons, and AR filters

🏥
Healthcare

Physical therapy monitoring and gait analysis

🔍 How It Works

1
Image Input

Feed image or video frame to the model

2
Person Detection

Locate humans in the image using object detection

3
Keypoint Detection

Identify body joint positions with confidence scores

4
Skeleton Assembly

Connect keypoints to form body skeleton structure

🎯 Single-Person Pose

Assumes one person per image. Faster and simpler, ideal for controlled environments.

Examples: PoseNet, MoveNet

👥 Multi-Person Pose

Detects multiple people simultaneously. More complex but handles crowded scenes.

Examples: OpenPose, AlphaPose

📊 Common Keypoint Standards

COCO (17 keypoints)

Nose, eyes, ears, shoulders, elbows, wrists, hips, knees, ankles

MPII (16 keypoints)

Head, neck, shoulders, elbows, wrists, hips, knees, ankles