Gesture Groove is an advanced computer vision project that implements real-time hand gesture recognition using cutting-edge machine learning algorithms and image processing techniques. This project represents the culmination of my image processing coursework, showcasing practical applications of computer vision in human-computer interaction.
Key Features
- Real-time hand gesture detection and recognition
- Multiple gesture classification with high accuracy
- Optimized for various lighting conditions
- Interactive user interface for gesture training
- Performance metrics and accuracy tracking
Technical Implementation
The project leverages OpenCV for image processing and MediaPipe for hand landmark detection. Machine learning models are trained using supervised learning techniques to classify different hand gestures with high precision.
Key technical achievements include implementing robust preprocessing pipelines, feature extraction algorithms, and real-time performance optimization for smooth user experience.
Learning Outcomes
This project enhanced my understanding of computer vision fundamentals, machine learning model training, and real-time system optimization. It provided hands-on experience with industry-standard tools and techniques used in gesture recognition systems.