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Samsung Gesture Gaming - TV Mirroring
A research project developing gesture recognition technology for TV mirroring and gaming interactions using OpenCV and machine learning algorithms.
Architecture Highlights
System architecture
- OpenCV-based real-time gesture detection and recognition.
- Machine learning algorithms for gesture classification and accuracy.
- Depth-sensing camera integration for 3D gesture tracking.
- Low-latency processing pipeline for real-time responsiveness.
- TV mirroring protocol implementation for seamless device connectivity.
Problem → Solution
Clear outcomes with a compliance mindset.
Traditional TV controls lack intuitive, hands-free interaction methods for gaming and media control.
Computer vision-based gesture recognition system using depth-sensing cameras and machine learning to enable natural user interactions with smart TVs.
Features
- Real-time gesture recognition with OpenCV computer vision.
- Multi-gesture support with customizable action mapping.
- TV mirroring functionality for mobile and smart TV integration.
- Performance optimization for accuracy, latency, and robustness.
- User experience testing framework for interaction improvement.
- Hardware compatibility testing across various configurations.
Tech
PythonOpenCVMachine LearningComputer VisionDepth-Sensing CamerasImage ProcessingAlgorithm Development
Impact
- Demonstrated feasibility of gesture-based TV control systems.
- Achieved low-latency gesture recognition for gaming applications.
- Contributed to research on natural user interfaces for smart TVs.
- Validated user acceptance through extensive UX testing.
- Documented algorithms and findings for team knowledge sharing.
Screenshots
Screenshots will be added after product updates.