Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation

Autor:	Hosseini, Babak, Montagne, Romain, Hammer, Barbara
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 19th IEEE International Conference on Data Mining (ICDM 2019)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.04969 Zobrazit plný text záznamu View this record from Arxiv