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HUMAN COMPUTER INTERACTION USING HAND GESTURES BY AFFORDABLE ALTERNATIVE TO
DEPTH CAMERA
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
THE TIMELINE
1977THE FIRST MASS MARKETED PERSONALCOMPUTER APPLE II
1982THE FIRST PC TO USE MODERN TRACKBALL BASEDMOUS
2015MODERN PC STILL USE THE THREE DECADE OLD MOUSEKEYBOARD SETUP
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
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.
THE INSPIRATION
THE CHALLENGES - HARDWARE
KINECT SENSOR
Rs 14,900Depth Camera
LEAP MOTION
Rs 8,900 Motion Sensor
LEAP MOTION
Rs 12,000 3D Sensor
OUR APPROACH - HARDWARE
IR ILLUMINATOR
Rs 120For IR Illumination
WebCam
Rs 1000For Vision Based Motion Sensing And Gesture recognistion
OUR APPROACH SOFTWARE
OPEN COMPUTER VISION LIBRARY WITH PYTHON
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.
HOW WE DO IT HARDWARE SOFTWARE
WHY USE THIS HARDWARE SETUP
BUILD THE CORE SOFTWARE
TRAINING THE SVM MODEL AND CHECKING THE ACCURACY
THE WORK FLOW
DETECTING AND
PREDICTING THE HAND POSE
EXPERIMENTS AND RESULTS
HAND CONTROLLER
VIRTUAL WHEEL
MULTI TOUCH
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
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
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.
THANK YOU