Hand Gesture Recognition Using OpenCV Python
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The project is about building a human-computer interaction system using hand gesture by cheap alternative to depth camera. We present a robust , efficient and real-time technique for depth mapping using normal 2D -camera and Infrared LED arrays . We use HOG feature based SVM classifiers to predict hand pose and dynamic hand gestures . The system also tracks hand movements and events like grabbing and clicking bythe hand.
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Hand Gesture Recognition Using OpenCV Python
- 1. HUMAN COMPUTER INTERACTION USING HAND GESTURES BY AFFORDABLE ALTERNATIVE TO DEPTH CAMERA
- 2. INTRODUCTION For the last three decades we are stuck at the tradtional mouse keyboard setup . Recently with the introduction of touch screen in smart phones and the emergance of Augmented Reality and Virtual Reality devices and state of the art sensors like Leap Motion and Kinect we are taking a leap into the future of human computer interaction
- 3. THE TIMELINE 1977 THE FIRST MASS MARKETED PERSONAL COMPUTER APPLE II 1982 THE FIRST PC TO USE MODERN TRACKBALL BASED MOUS 2015 MODERN PC STILL USE THE THREE DECADE OLD MOUSE KEYBOARD SETUP
- 4. WHY CHANGE? With The introduction of Virtual Reality and Augmented Reality deivecs this tradtional mouse keyboard setup is of no use . We just can't intteract with a Virtual Reality System with a mouse and keyboard
- 5. THE OBJECTIVE Our Objective is to built a low cost system with help of low cost hardware and open source software to provide robust and accurate hand gesture recognition and tracking.
- 7. THE CHALLENGES - HARDWARE KINECT SENSOR Rs 14,900 Depth Camera LEAP MOTION Rs 8,900 Motion Sensor LEAP MOTION Rs 12,000 3D Sensor
- 8. OUR APPROACH - HARDWARE IR ILLUMINATOR Rs 120 For IR Illumination WebCam Rs 1000 For Vision Based Motion Sensing And Gesture recognistion
- 9. OUR APPROACH SOFTWARE OPEN COMPUTER VISION LIBRARY WITH PYTHON
- 10. THE PROJECT To build a hand gesture recognition system that doesn’t get affected by external factors such as light , distance and movements. To build a system that can recognize with high accuracy. To build a system that can help us interact with a computer with ease.
- 11. HOW WE DO IT HARDWARE SOFTWARE
- 12. WHY USE THIS HARDWARE SETUP
- 13. BUILD THE CORE SOFTWARE
- 14. TRAINING THE SVM MODEL AND CHECKING THE ACCURACY
- 15. THE WORK FLOW
- 18. HAND CONTROLLER
- 19. VIRTUAL WHEEL
- 20. MULTI TOUCH
- 21. LIMITATIONS AND IMPROVEMENT LIMITATION FPS limitations cannot detect fast moving hand The System makes an assumption that the hand is the closest and the brightest object to the camera
- 22. LIMITATION AND IMPROVEMENT IMPROVEMENTS Use a common Light source Use a IR illuminator and a band pass filter to filter out unnecessary background objects Use of IR illuminator to estimate depth Use of High FPS webcam as image source Using a depth camera to understand the scene Using machine learning techniques to estimate hand pose
- 23. FUTURE IMPROVEMENTS • FUTURE SCOPE • Using more sophisticated machine learning techniques • Try to build a complete product . • Miniaturization of the whole system. • Running the device with least power.
- 24. THANK YOU