# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
## 1 Introduction
For example, in the literature [9; 10], the original coordinates were directly utilized to depict human motions, leading to unsatisfactory results. Because absolute motion data are easily affected by variations in viewpoint and human appearance. Although the authors [14] obtained excellent accuracy, the methods that use absolute motion data will inevitably meet dataset-based or camera-based problems. In [11], the authors adopted a sequence of joint angles to label human motions. However, only using geometrical information is insufficient to describe an action. For example, \"sitting down\" and \"standing up\" have similar spatial motion data but have different motion data in the temporal dimension. In addition, when training CNN models on small-scale datasets, overfitting is a fatal issue that needs special attention. To address the mentioned issues, we propose a novel skeleton-based action recognition method using a CNN model. In our proposed method, the spatial-temporal motion data of human action are encoded into an image-based representation. According to [15], the relative features can provide a view-invariant representation of human actions. Hence, three relative features are extracted, frame-by-frame, to describe human motions more accurately. The three relative features are consisted of relative coordinates, immediate displacement, and immediate motion orientation. In the following step, these feature data are stored in feature matrices and are normalized to (0, 255) to fit the storing requirement of the RGB channels and transformed into a color image. Furthermore, to cope with varying-length skeleton sequences, an effective skeleton sequence refinement strategy is utilized to align action sequences. By this way, the generated motion images have consistent spatial sizes. In addition, in response to the issue of the limited size of training samples, a data augmentation strategy is proposed to generate samples and prevent the CNN model from overfitting. Finally, a shallow CNN model is sufficient to efficiently extract deep features because of the small size of our proposed skeleton-based motion image. Moreover, the proposed CNN model guarantees the high effectiveness of our method. The main research contributions of this paper are summarized as follows:  
1. A relative coordinate is introduced to describe human motions, which not only depicts the spatial relations of human skeleton joints but also provides long-term temporal characteristics. 2. An effective skeleton sequence refinement strategy, including an average linear interpolation algorithm and a randomly discard frame approach, is proposed to address varying-length action sequences. 3. A human action-oriented data augmentation strategy with a strong generalization capacity is proposed to generate action samples for recognition accuracy improvements and overfitting prevention. The remaining parts of this paper are structured as follows: In Section 2, the related works in the field of action recognition using skeleton data are reviewed. Then, our proposed HAR method is elaborated in Section 3. Section 4 presents experiments on the two small datasets. Finally, the conclusion of the paper is given in Section 5.