Affiliation:
1. School of Electrical and Information Engineering, Tongji University, Shanghai 201804, China
2. Tongji Research Institute of Artificial Intelligence (Suzhou), Suzhou 215300, China
Abstract
Graph Convolution Network (GCN) algorithms have greatly improved the accuracy of skeleton-based human action recognition. GCN can utilize the spatial information between skeletal joints in subsequent frames better than other deep learning algorithms, which is beneficial for achieving high accuracy. However, the traditional GCN algorithms consume lots of computation for the stack of multiple primary GCN layers. Aiming at solving the problem, we introduce a lightweight network, a Differential Learning and Parallel Convolutional Networks (DL-PCN), whose key modules are Differential Learning (DLM) and the Parallel Convolutional Network (PCN). DLM features a feedforward connection, which carries the error information of GCN modules with the same structure, where GCN and CNN modules directly extract the original information from the input data, making the spatiotemporal information extracted by these modules more complete than that of GCN and CNN tandem structure. PCN comprises GCN and Convolution Neural Network (CNN) in parallel. Our network achieves comparable performance on the NTU RGB+D 60 dataset, the NTU RGB+D 120 dataset and the Northwestern-UCLA dataset while considering both accuracy and calculation parameters.
Reference39 articles.
1. Y. Chen, Z. Zhang, C. Yuan et al., Channel-wise topology refinement graph convolution for skeleton-based action recognition, in: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13339–13348.
2. Y. Chen, Z. Zhang, C. Yuan et al., Channel-wise topology refinement graph convolution for skeleton-based action recognition, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 13359–13368.
3. K. Cheng, Y. Zhang, C. Cao et al., Decoupling GCN with dropgraph module for skeleton-based action recognition, in: European Conference on Computer Vision, Springer, 2020, pp. 536–553.
4. Y. Du, W. Wang, L. Wang et al., Hierarchical recurrent neural network for skeleton based action recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1110–1118.
5. PYSKL: Towards Good Practices for Skeleton Action Recognition