Data Augmentation for Human Keypoint Estimation Deep Learning based Sign Language Translation

Abstract

Deep learning technology has developed constantly and is applied in many fields. In order to correctly apply deep learning techniques, sufficient learning must be preceded. Various conditions are necessary for sufficient learning. One of the most important conditions is training data. Collecting sufficient training data is fundamental, because if the training data are insufficient, deep learning will not be done properly. Many types of training data are collected, but not all of them. So, we may have to collect them directly. Collecting takes a lot of time and hard work. To reduce this effort, the data augmentation method is used to increase the training data. Data augmentation has some common methods, but often requires different methods for specific data. For example, in order to recognize sign language, video data processed with openpose are used. In this paper, we propose a new data augmentation method for sign language data used for learning translation, and we expect to improve the learning performance, according to the proposed method.