Vision Based Gesture Recognition from RGB Video frames Using Morphological Image Processing Techniques
with the large number of population in all over the world nowadays, novel human computer interaction systems and techniques can be used to help improve our way of life. A vision based gesture recognition technology can help to maintain the safety and needs of the disable as well as others. Gesture recognition from video frames is a challenging task due to the high changeability in the features of each gesture with respect to different person. In this work, we propose a vision-based hand gesture recognition algorithm where the image frames are from RGB video data. Gesture-based systems are more natural, spontaneous, and straightforward. Previous works attempted to recognize hand gesture for different kind of scenarios. According to our studies, gesture recognition system can be based on wearable sensor or it can be vision based. Our proposed method is applied on a vision based gesture recognition system. In our proposed system image acquisition starts from RGB videos capture using Kinect sensor. We convert the image frames one after another from videos to blur for background noise removal. Then, we convert the images of a whole video into HSV color mode. After that, we do the dilation, erosion, filtering, and thresholding operations on the images. We use these morphological image processing techniques for converting the images to black and white format. Finally, using the prominent classification algorithm SVM we recognize the hand gestures with a higher accuracy 91.01 percent compared to the state of the art. In conclusion, the proposed algorithm aims to create a better vision-based hand gesture recognition system with a unique solution in this domain.