Augmenting SpatioTemporal Human Motion Data for Effective 3D Action Recognition Action recognition is a fundamental operation in 3D human motion analysis. Existing deep learning classifiers achieve a high recognition accuracy if large amounts of training data are provided. However, such data are difficult to obtain in a variety of application scenarios, mainly due to the high costs of motion capture technologies and an absence of suitable actors. In this paper, we propose augmentation techniques to artificially enlarge existing collections of 3D human skeleton sequences. The proposed techniques are especially useful for datasets distinguishing in a high number of classes, each of them characterized by only a limited number of action samples. We experimentally demonstrate that the augmented data help to significantly increase the recognition accuracy even using a standard deep learning architecture.

Data Augmentation For CNNBased 3D Action Recognition on SmallScale Datasets Videobased human action recognition recently plays a vital role in many industrial applications thanks to the popularity of depth sensors. A large number of conventional approaches, which have combined handcrafted features and traditional classifiers, cannot deal with various challenges in the field such as the complexity of human actions in the realistic environment. In order to improve recognition performance by exploiting more highlevel discriminative features, an efficient skeletonbased action recognition method using deep convolutional neural networks (CNNs) is studied with an image encoder to transform skeleton coordinate data to imageformed data. Since deep learning techniques are fundamentally designed for efficiently working with large datasets, the network overfitting usually occurs if training CNNs on smallscale datasets. To address this issue, a novel data augmentation technique is proposed for both the informative enrichment and overfitting prevention, wherein a skeleton sequence is depicted by manifold action images based on randomly adding some skeleton frames during the data transformation and preparation for the training set. Experimental results on several smallscale challenging datasets demonstrate that the proposed method outperforms stateoftheart approaches in terms of action recognition accuracy.