Abstract
This paper introduces a hand gesture recognition algorithm for Human Computer Interaction using real-time video streaming .The background subtraction technique is used to extract the ROI (Region Of Interest) of the hand. Fingertip is detected using logical heuristics equations that are applied on hand contour , convex hull and convexity defects points. A combination between background subtraction and logical heuristic technique that leads to more accurate results is introduced. Experimental results prove that the proposed algorithm improve the finger's tips detection by 52 % compared to the reference model.
Highlights
Gesture recognition is a challenging area in computer vision
Once hand gestures are recognized, it will make the interaction much better for the user .This paper proposes an algorithm to extract foreground hand regions from background using background averaging and extracting contour, the convex hull and convexity defects information to recognize each gesture
There are different techniques that have been used for hand gesture recognition in different fields
Summary
Gesture recognition is a challenging area in computer vision. At present, many traditional interfaces such as the keyboard or mouse doesn't provide the best interaction between user and computer. Some of the techniques using skin model , colored gloves , data gloves , PCA , Neural Network. All techniques can be divided into the following categories : Hand segmentation techniques , Feature extraction techniques and Gesture recognition techniques. In the paper [9] data glove package that contains a Bluetooth , 3 accelerometer sensor, and a controller which extract hand gesture features. In paper [12] Background averaging and CAMSHIFT algorithms are used for hand tracking in real time and test only two gestures.
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