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Hand Gesture Recognition Using OpenCV Python
The document discusses the development of an affordable hand gesture recognition system as an alternative to the traditional mouse and keyboard setup for human-computer interaction, particularly with the rise of virtual and augmented reality. The project aims to create a reliable and low-cost solution using basic hardware and open source software, overcoming limitations such as light interference and motion speed. Future improvements are expected to enhance accuracy, miniaturization, and power efficiency.
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Hand Gesture Recognition Using OpenCV Python
- 1. HUMAN COMPUTER INTERACTIONUSING HAND GESTURES BY AFFORDABLE ALTERNATIVE TO DEPTH CAMERA
- 2. INTRODUCTION For the lastthree 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 FIRSTMASS 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 Theintroduction 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 Objectiveis 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.
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- 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 OPENCOMPUTER VISION LIBRARY WITH PYTHON
- 10. THE PROJECT Tobuild 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 DOIT HARDWARE SOFTWARE
- 12. WHY USE THISHARDWARE SETUP
- 13. BUILD THE CORESOFTWARE
- 14. TRAINING THE SVMMODEL AND CHECKING THE ACCURACY
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- 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 • FUTURESCOPE • Using more sophisticated machine learning techniques • Try to build a complete product . • Miniaturization of the whole system. • Running the device with least power.
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