With the growing number of population in the world nowadays, novel human-computer interactionsystems and techniques can be used to help improve their quality of life. A gesture based technologycan help to maintain the safety and needs of the disable as well as the general people. Gesturerecognition from video streams is a challenging task due to the high changeability in the features ofeach gesture with respect to different person. In this work, we propose a vision-based hand gesturerecognition from RGB video data using SVM.Gesture-based interfaces are more natural, spontaneous, and straightforward. Previous worksattempted to recognize hand gesture for different scenarios. Throughout our studies, gesturerecognition system can be based on wearable sensor or it can be vision based. Our proposed methodis applied on a vision based gesture recognition system. In our proposed system image acquisitionstarts from RGB videos capture using Kinect sensor. We convert the image frames from videos toblur for background noise removal. Then, we convert the images into HSV color mode. After that,we perform the dilation, erosion, filtering, and thresholding the image for converting to black andwhite format. Finally, using the prominent classification algorithm SVM, hand gestures have beenrecognized. In conclusion, the framework aims to create a better vision-based hand gesturerecognition system with novel techniques.
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