# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
## 1 Introduction  
In recent years, HAR has received increasing attention in the field of computer vision. Because it has a wide range of industrial applications, such as human computer interaction, smart video surveillance, and health care [1]. However, there are endemic problems, such as action diversity, partial occlusion of subjects, which hinder the development and commercial application of HAR. The mainstream applications for HAR use RGB images or videos with the aim of simulating human vision. Motion history images and optical flows are two commonly used motion images for color-image-based methods. In the literature [2], [PERSON] et al. adopted motion-energy images and motion-history images to separately store motion locations and motion intensities. To produce better video representations for HAR, a number of researchers began to use a two-stream CNN to capture appearance features and motion features, and fused the classification scores of the two streams to generate the final result [3]. In addition, a few approaches employed static images; for example, [PERSON] et al. [4] proposed a novel still image that contained optical flows and \"dynamicimages\" of temporal representations. Although RGB data can provide rich scene contexts and are easy to collect, RGB data are sensitive to viewpoint, background, and illumination conditions [5]. With the development of depth cameras, depth images based on HAR have received the attention of researchers. Compared with RGB images, depth images more easily segment foreground human subjects even though the scene is cluttered and can provide more geometric information [1; 5]. In [6], the authors created three dynamic depth images to fully exploit the spatial, temporal, and structural information in a depth sequence for action recognition at different timescales. To better exploit 3D characteristics, [PERSON] et al. [7] densely projected the raw depth video to different imaging viewpoints within a 3D space to construct multiview dynamic images. Nevertheless, depth images also have drawbacks; for example, they are limited by workable distances and are vulnerable to noise [1; 5]. With the increasing use of algorithms to capture the 3D coordinates of human skeletal joints, the skeleton-based human action recognition research area is emerging. Compared with former methods, skeleton data can provide 3D structural and pose location information, making motion representation simpler and more informative. Furthermore, skeleton data have a lower computational load and are more robust when dealing with changing conditions, such as different body scales and motion speeds [1; 5]. Similar to color-image-based methods, skeleton-based methods can also be divided into two categories, handcrafted feature-based methods and deep learning feature-based methods. To form a final feature descriptor, HAR using handcrafted features has two stages, feature extraction and feature representation. In the former stage, various sorts and varieties of motion features are proposed, such as the relative coordinates and angles between joints. The feature representation stage may simply concatenate the features that are obtained in the first stage or use a machine learning technique to obtain the final features. Skeleton-based HAR of handcrafted feature-based methods is shown in [8; 9; 10; 11]. Different from handcrafted feature-based methods, deep learning feature-based methods can automatically extract and learn multiple high-level features from video sequences or skeleton data by constructing multiple connected convolutional layer stacks. In recent years, HAR with deep learning feature-based methods has been reported in articles [12; 13; 14]. However, deep CNNs require a large number of labelled training samples to obtain peak performance. So, deep learning feature-based methods cannot provide noteworthy results for small sample datasets [1]. Although skeleton-based HAR methods have been greatly improved, some deficiencies still cannot be ignored.