Abstract
Computers and computerized machines have tremendously penetrated all aspects of our lives. This raises the importance of Human-Computer Interface (HCI). The common HCI techniques still rely on simple devices such as keyboard, mice, and joysticks, which are not enough to convoy the latest technology. Hand gesture has become one of the most important attractive alternatives to existing traditional HCI techniques. This paper proposes a new hand gesture detection system for Human-Computer Interaction using real-time video streaming. This is achieved by removing the background using average background algorithm and the 1$ algorithm for hand’s template matching. Then every hand gesture is translated to commands that can be used to control robot movements. The simulation results show that the proposed algorithm can achieve high detection rate and small recognition time under different light changes, scales, rotation, and background.
Highlights
Computers and computerized machines have tremendously penetrated all aspects of our lives. This raises the importance of Human-Computer Interface (HCI)
This paper proposes a system for hand gesture detection by using the average background algorithm [31] for background subtraction and the 1$ algorithm [19] for hand’s template matching
The first stage is the background subtraction algorithm that is used to remove all static objects that reside in the background, and extracting the region of interest that contains the hand gesture
Summary
Computers and computerized machines have tremendously penetrated all aspects of our lives. The authors in [18] have proposed a new system for detecting and tracking bare hand in cluttered background They use multiclass support vector machine (SVM) for classification and K-means for clustering. The reason is that some objects such as human arm and face have colour similar to the hand To solve this problem, modified 1$ algorithm [19] is used in this paper to extract hand gestures with high accuracy. Background subtraction has direct effect on the accuracy and computational complexity of hand gestures extraction algorithm [20, 21]. The simulation results of the proposed system show that the hand gesture detection rate is 98% as well as the computational time complexity is improved.
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