Abstract
Due to the small size, anti-interference and strong robustness of skeletal data, research on human skeleton-based action recognition has become a mainstream. However, due to the incomplete utilization of semantic information and insufficient time modeling, most methods may not be able to fully explore the connections between non-adjacent joints in the spatial or temporal dimensions. Therefore, we propose a Multi-scale Dilated Attention Graph Convolutional Network for Skeleton-Based Action Recognition (MDKA-GCN) to solve the above problems. In the spatial configuration, we explicitly introduce the channel graph composed of high-level semantics (joint type and frame index) of joints into the network to enhance the representation ability of spatiotemporal features. MDKA-GCN uses joint-level, velocity-level and bone-level graphs to more deeply mine the hidden features of human skeletons. In the time configuration, two lightweight multi-scale strategies are proposed, which can be more robust to time changes. Extensive experiments on NTU-RGB+D 60 datasets and NTU-RGB+D 120 datasets show that MDKA-GCN has reached an advanced level, and surpasses the performance of most lightweight SOTA methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Carreira, J., Zisserman, A.: Quo vadis, action recognition? A new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017)
Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Si, C., Chen, W., Wang, W., Wang, L., Tan, T.: An attention enhanced graph convolutional LSTM network for skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1227–1236 (2019)
Xu, K., Ye, F., Zhong, Q., Xie, D.: Topology-aware convolutional neural network for efficient skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 2866–2874 (2022)
Shi, L., Zhang, Y., Cheng, J., Lu, H.: Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12026–12035 (2019)
Qin, Z., et al.: Fusing higher-order features in graph neural networks for skeleton-based action recognition. IEEE Trans. Neural Netw. Learn. Syst., 1–15 (2022)
Wen, Y.H., Gao, L., Fu, H., Zhang, F.L., Xia, S., Liu, Y.J.: Motif-GCNS with local and non-local temporal blocks for skeleton-based action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 2009–2023 (2022)
Liu, Y., Zhang, H., Li, Y., He, K., Xu, D.: Skeleton-based human action recognition via large-kernel attention graph convolutional network. IEEE Trans. Visual Comput. Graphics 29(5), 2575–2585 (2023)
Zhang, P., Lan, C., Zeng, W., Xing, J., Xue, J., Zheng, N.: Semantics-guided neural networks for efficient skeleton-based human action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1112–1121 (2020)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Wang, Y., Li, Y., Wang, G., Liu, X.: Multi-scale attention network for single image super-resolution. arXiv preprint arXiv:2209.14145 (2022)
Guo, M.H., Lu, C.Z., Liu, Z.N., Cheng, M.M., Hu, S.M.: Visual attention network. arXiv preprint arXiv:2202.09741 (2022)
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)
Cheng, K., Zhang, Y., He, X., Chen, W., Cheng, J., Lu, H.: Skeleton-based action recognition with shift graph convolutional network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 183–192 (2020)
Cheng, K., Zhang, Y., He, X., Cheng, J., Lu, H.: Extremely lightweight skeleton-based action recognition with shiftGCN++. IEEE Trans. Image Process. 30, 7333–7348 (2021)
Song, Y.F., Zhang, Z., Shan, C., Wang, L.: Constructing stronger and faster baselines for skeleton-based action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 1474–1488 (2022)
Zhang, H., Zu, K., Lu, J., Zou, Y., Meng, D.: EPSANet: an efficient pyramid split attention block on convolutional neural network. arxiv 2021. arXiv preprint arXiv:2105.14447 (2021)
Shahroudy, A., Liu, J., Ng, T.T., Wang, G.: NTU RGB+ D: a large scale dataset for 3d human activity analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1010–1019 (2016)
Liu, J., Shahroudy, A., Perez, M., Wang, G., Duan, L.Y., Kot, A.C.: NTU RGB+ D 120: a large-scale benchmark for 3d human activity understanding. IEEE Trans. Pattern Anal. Mach. Intell. 42(10), 2684–2701 (2019)
Liu, Z., Zhang, H., Chen, Z., Wang, Z., Ouyang, W.: Disentangling and unifying graph convolutions for skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 143–152 (2020)
Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., Tian, Q.: Symbiotic graph neural networks for 3d skeleton-based human action recognition and motion prediction. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 3316–3333 (2021)
Miao, S., Hou, Y., Gao, Z., Xu, M., Li, W.: A central difference graph convolutional operator for skeleton-based action recognition. IEEE Trans. Circuits Syst. Video Technol. 32(7), 4893–4899 (2021)
Duan, H., Wang, J., Chen, K., Lin, D.: PYSKL: towards good practices for skeleton action recognition. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 7351–7354 (2022)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (61976006).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shu, Y., Li, W., Li, D., Gao, K., Jie, B. (2024). Multi-scale Dilated Attention Graph Convolutional Network for Skeleton-Based Action Recognition. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14425. Springer, Singapore. https://doi.org/10.1007/978-981-99-8429-9_2
Download citation
DOI: https://doi.org/10.1007/978-981-99-8429-9_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8428-2
Online ISBN: 978-981-99-8429-9
eBook Packages: Computer ScienceComputer Science (R0)