A Deep End-to-end Hand Detection Application On Mobile Device Based On Web Of Things

Abstract

In this paper, a novel end-to-end hand detection method YOLObile-KCF on mobile device based on Web of Things (WoT) is presented, which can also be applied in practice. While hand detection has been become a hot topic in recent years, little attention has been paid to the practical use of hand detection on mobile device. It is demonstrated that our hand detection system can effectively detect and track hand with high accuracy and fast speed that enables us not only to communicate with each other on mobile devices, but also can assist and guide the people on the other side on the mobile device in real-time. The method used in our study is known as object detection, which is a working theory based on deep learning. And lightweight neural network suitable for mobile device which can has few parameters and easily deployed is adopted in our model. What's more, KCF algorithms is added in our model. And several experiments were carried out to test the validity of hand detection system. From the experiment, it came to realize that the YOLObile-KCF hand detection system based on WoT is considerable, which is more efficient and convenient in smart life. Our work involving studies of hand detection for smart life proves to be encouraging.