  Convolutional neural networks (CNNs) are deep learning frameworks which are
well-known for their notable performance in classification tasks. Hence, many
skeleton-based 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, non-uniform temporal scalings is a common issue in
skeleton-based data streams which leads to having different input sizes even
within one specific action category. In this work, we propose a novel
deep-aligned 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 real-world
benchmarks showing that the proposed DACNN algorithm obtains a competitive
performance compared to the state-of-the-art while benefiting from a less
complicated yet more interpretable model.
