Welcome to the Hand Tracking project repository! This project utilizes Google's Mediapipe framework to implement a robust hand tracking system, capable of detecting and tracking 20 key landmarks on each hand in real-time.
Features Real-time Hand Detection: Instantly detects hands in real-time, providing immediate feedback even with complex hand movements. 20 Hand Landmarks: Identifies 20 specific landmarks on the hand, including key points on the fingers and palm, enabling detailed hand pose estimation. High Accuracy and Efficiency: Optimized for accuracy and efficiency, capable of running on various devices including mobile phones and web browsers. Gesture Recognition: Analyzes the relative positions and movements of the landmarks to recognize various hand gestures, essential for applications such as sign language recognition and human-computer interaction (HCI). Cross-platform Compatibility: Deployable across multiple platforms, thanks to Mediapipe’s compatibility with Android, iOS, and web technologies.
Technical Details Mediapipe Framework: Utilizes Mediapipe’s pre-trained hand tracking model as the core component. Landmark Detection: Processes input video frames to detect hands and identify 20 key landmarks:
- Wrist (0)
- Thumb (1-4)
- Index Finger (5-8)
- Middle Finger (9-12)
- Ring Finger (13-16)
- Little Finger (17-20) Pipeline Optimization: Optimized to reduce latency and increase processing speed for smooth real-time performance. Gesture Mapping: Custom algorithms map detected landmarks to predefined gestures for gesture recognition.
Applications Sign Language Interpretation: Translate hand signs into text or speech for communication assistance. Virtual Reality (VR): Enhance user experience with intuitive hand-based interactions in VR environments. Augmented Reality (AR): Interact with virtual objects overlaid on the real world using hand tracking. Human-Computer Interaction (HCI): Develop touchless interfaces for controlling applications with hand gestures.
Getting Started To get started with this project, follow the steps below:
Prerequisites
- Python 3.x
- Mediapipe
- OpenCV
License This project is licensed under the MIT License. See the LICENSE file for details.
Acknowledgments Thanks to Google Mediapipe for providing the hand tracking model and tools. Inspired by the potential of hand tracking in various applications.