Pose Detection System
Real-time pose & hand detection using MediaPipe with 33 skeletal keypoints and 21 finger joints per hand
Full Body Tracking & Hand Detection
Real-time pose estimation and hand detection system using MediaPipe, applied to YouTube video feeds. Track full body movements with 33 skeletal keypoints and detailed hand analysis with 21 finger joint positions per hand.
The system uses browser-native MediaPipe models with a Python Flask proxy server for YouTube video streaming, enabling real-time skeleton and hand landmark rendering with performance metrics.
Detection Capabilities
Core Features
Full Body Pose Detection
Track 33 skeletal keypoints including nose, shoulders, elbows, wrists, hips, knees, and ankles for complete body movement analysis.
Hand Landmark Detection
Identify both hands with 21 detailed finger joint positions per hand for precise gesture recognition and tracking.
Visual Overlay Rendering
Real-time skeleton and hand landmark visualization overlaid on video with smooth, responsive rendering.
Performance Metrics
FPS counter and detection point statistics displaying frames per second and number of detected keypoints.
Interactive Controls
Toggle pose and hand detection independently, adjust confidence thresholds from 0-100%, and live status indicators.
YouTube Video Integration
Flask proxy server enables CORS-compliant YouTube video streaming for browser-based MediaPipe processing.
Technical Specifications
Ready to Track Human Movement?
Deploy real-time pose and hand detection with 33+21 landmark precision