Mudras and Yoga Positions Detection and Recognition using YOLOv7 and Faster R-CNN

Hand gesture recognition is a computer vision technique that involves the automatic identification of hand gestures and movements. This method has attracted substantial interest owing to its potential utility in various domains, such as computer-mediated communication between humans and machines, identification of sign language gestures, and simulation environments. The process of hand gesture recognition consists of several steps, including image acquisition, preprocessing, feature extraction, and classification. Different techniques, such as convolutional neural networks, deep belief networks, and support vector machines, have been proposed for each of these steps to detect yoga positions. Nevertheless, the task of hand gesture recognition continues to present significant challenges because of the wide range and complexity inherent in human hand gestures. Therefore, this research aims to explore the latest techniques and advancements in hand gesture recognition and propose a robust and accurate method for real-time hand gesture recognition in various scenarios. In order to assess the efficacy of the proposed approach, standard datasets for hand gesture recognition will be employed as a means of evaluation. Subsequently, a comparison between our method and existing state-of-the-art techniques will be conducted. This research has the potential to contribute to the development of future applications of hand gesture recognition.