Khushi Gupta, Arshdeep Singh, Sreenivasa Reddy Yeduri, M B Srinivas, Linga Reddy Cenkeramaddi, “Hand Gestures Recognition using Edge Computing System based on Vision Transformer and Lightweight CNN,” has been accepted for publication in the Journal of Ambient Intelligence and Humanized Computing (2022).
Keywords: Hand gesture recognition, NUS Hand Posture Dataset I, Turkey Ankara Ayrancı Anadolu High School’s Sign Language Digits Dataset, American Sign Language dataset, Vision transformer, Convolutional Neural Network, Edge computing device, Raspberry Pi, MobileNet, ResNet, VGGNet
Abstract: Human computer interaction, human-robot interaction, robotics, healthcare systems, health assistive technologies, automotive user interfaces, crisis management, disaster relief, entertainment, and contactless communication in smart devices are just a few of the practical applications for hand gesture recognition. In this work, we propose two novel machine learning models for hand gesture recognition using three publicly available datasets, NUS Hand Posture Dataset I, Turkey Ankara Ayrancı Anadolu High School’s Sign Language Digits Dataset, and American Sign Language dataset. The developed models based on vision transformer and lightweight Convolutional Neural Networks are deployed on an end-to-end edge computing system that can accurately provide the classification of hand gestures. The edge computing system presented here utilizes Raspberry Pi. The designed models achieve an accuracy of 90–99% on the test datasets. The performance of the proposed models is also compared to the pre-trained models such as MobileNet, ResNet, and VGGNet.
More details:DOI: https://doi.org/10.1007/s12652-022-04506-4