This project aims to create a gesture-based control system for playing the classic game Hill Climb Racing using hand gestures. Leveraging computer vision techniques through OpenCV, users can control the game without traditional input devices.
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Description: Created a gesture-based control system for playing the classic game Hill Climb Racing using hand gestures. This project utilized computer vision techniques through OpenCV to detect and interpret hand gestures, allowing users to control the game without traditional input devices.
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Key Technologies:
- OpenCV: Used for real-time hand detection and gesture recognition.
- cvzone: Leveraged the HandTrackingModule for streamlined hand detection.
- Real-time Hand Detection Implemented a hand detection algorithm to identify hand movements through the webcam.
- Gesture Recognition: Utilized finger position tracking to recognize specific gestures corresponding to game controls.
- Keyboard Simulation: Interfaced with the game via simulated keyboard inputs, enabling seamless control based on detected gestures.
- Responsive Gameplay: Provided a dynamic and immersive gaming experience, allowing players to interact with the game using intuitive hand gestures.
To set up the project, install the required libraries and dependencies using the following commands:
pip install opencv-python
pip install cvzone
further, setup the file structure as given in my gitbhub repository.
Successfully developed a novel control mechanism for Hill Climb Racing, enhancing user experience through gesture-based interaction. This project demonstrates the potential of computer vision technology in gaming applications, offering an innovative and engaging gameplay experience.
- Enhance gesture recognition accuracy for smoother gameplay.
- Integrate additional gestures for expanded control options.
- Optimize performance for better responsiveness and compatibility with various hardware configurations.