HandTracking - Real-time Hand Detection

Overview

Real-time hand detection system designed specifically for egocentric (first-person) video views. Trained on the EgoHands dataset, this lightweight model detects hand bounding boxes in video streams and can output labeled videos with hand annotations. Ideal for quick prototyping of hand-based interaction systems in AR/VR and wearable computing applications.

Companion JavaScript library: Handtrack.js is available for browser-based applications (https://github.com/victordibia/handtrack.js).

When to Use This Skill

This skill should be used when:

• Analyzing egocentric video footage from wearable cameras or smart glasses
• Detecting hand presence and location in first-person perspective videos
• Building hand gesture interfaces or interaction systems
• Annotating training data for hand detection models
• Processing egocentric videos for human-computer interaction research
• Creating labeled video outputs with hand bounding box overlays
• Implementing real-time hand detection in web applications (using Handtrack.js)
• Quick prototyping of hand-based AR/VR interfaces