DeepAligned Convolutional Neural Network for SkeletonBased Action Recognition and Segmentation Convolutional neural networks (CNNs) are deep learning frameworks which are wellknown for their notable performance in classification tasks. Hence, many skeletonbased action recognition and segmentation (SBARS) algorithms benefit from them in their designs. However, a shortcoming of such applications is the general lack of spatial relationships between the input features in such data types. Besides, nonuniform temporal scalings is a common issue in skeletonbased data streams which leads to having different input sizes even within one specific action category. In this work, we propose a novel deepaligned convolutional neural network (DACNN) to tackle the above challenges for the particular problem of SBARS. Our network is designed by introducing a new type of filters in the context of CNNs which are trained based on their alignments to the local subsequences in the inputs. These filters result in efficient predictions as well as learning interpretable patterns in the data. We empirically evaluate our framework on realworld benchmarks showing that the proposed DACNN algorithm obtains a competitive performance compared to the stateoftheart while benefiting from a less complicated yet more interpretable model.

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.