{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T10:28:38Z","timestamp":1778149718154,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:00:00Z","timestamp":1689292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"publisher","award":["2022NSFSC0553"],"award-info":[{"award-number":["2022NSFSC0553"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"publisher","award":["62020106010"],"award-info":[{"award-number":["62020106010"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"publisher","award":["GSJDJS2021006"],"award-info":[{"award-number":["GSJDJS2021006"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022NSFSC0553"],"award-info":[{"award-number":["2022NSFSC0553"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62020106010"],"award-info":[{"award-number":["62020106010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["GSJDJS2021006"],"award-info":[{"award-number":["GSJDJS2021006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Construction Project of Demonstration Practice Base for Professional Degree Postgraduates","award":["2022NSFSC0553"],"award-info":[{"award-number":["2022NSFSC0553"]}]},{"name":"Construction Project of Demonstration Practice Base for Professional Degree Postgraduates","award":["62020106010"],"award-info":[{"award-number":["62020106010"]}]},{"name":"Construction Project of Demonstration Practice Base for Professional Degree Postgraduates","award":["GSJDJS2021006"],"award-info":[{"award-number":["GSJDJS2021006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Graph convolutional networks (GCNs), which extend convolutional neural networks (CNNs) to non-Euclidean structures, have been utilized to promote skeleton-based human action recognition research and have made substantial progress in doing so. However, there are still some challenges in the construction of recognition models based on GCNs. In this paper, we propose an enhanced adjacency matrix-based graph convolutional network with a combinatorial attention mechanism (CA-EAMGCN) for skeleton-based action recognition. Firstly, an enhanced adjacency matrix is constructed to expand the model\u2019s perceptive field of global node features. Secondly, a feature selection fusion module (FSFM) is designed to provide an optimal fusion ratio for multiple input features of the model. Finally, a combinatorial attention mechanism is devised. Specifically, our spatial-temporal (ST) attention module and limb attention module (LAM) are integrated into a multi-input branch and a mainstream network of the proposed model, respectively. Extensive experiments on three large-scale datasets, namely the NTU RGB+D 60, NTU RGB+D 120 and UAV-Human datasets, show that the proposed model takes into account both requirements of light weight and recognition accuracy. This demonstrates the effectiveness of our method.<\/jats:p>","DOI":"10.3390\/s23146397","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T08:40:06Z","timestamp":1689324006000},"page":"6397","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Enhanced Adjacency Matrix-Based Lightweight Graph Convolution Network for Action Recognition"],"prefix":"10.3390","volume":"23","author":[{"given":"Daqing","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China"}]},{"given":"Hongmin","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China"}]},{"given":"Yong","family":"Zhi","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104146","DOI":"10.1016\/j.infrared.2022.104146","article-title":"AGCNNs: Attention-guided convolutional neural networks for infrared head pose estimation in assisted driving system","volume":"123","author":"Ju","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/34.982883","article-title":"Detecting faces in images: A survey","volume":"24","author":"Yang","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1109\/TSMCC.2004.829274","article-title":"A survey on visual surveillance of object motion and behaviors","volume":"34","author":"Hu","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1109\/TCSVT.2008.2005594","article-title":"Machine recognition of human activities: A survey","volume":"18","author":"Turaga","year":"2008","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103859","DOI":"10.1016\/j.infrared.2021.103859","article-title":"Action recognition of individuals on an airport apron based on tracking bounding boxes of the thermal infrared target","volume":"117","author":"Ding","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MMUL.2012.24","article-title":"Microsoft kinect sensor and its effect","volume":"19","author":"Zhang","year":"2012","journal-title":"IEEE Multimed."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"201","DOI":"10.3758\/BF03212378","article-title":"Visual perception of biological motion and a model for its analysis","volume":"14","author":"Johansson","year":"1973","journal-title":"Percept. Psychophys."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104164","DOI":"10.1016\/j.infrared.2022.104164","article-title":"R-Net: A novel fully convolutional network-based infrared image segmentation method for intelligent human behavior analysis","volume":"123","author":"Chen","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kim, T., and Reiter, A. (2017, January 10\u201314). Interpretable 3d human action analysis with temporal convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.207"},{"key":"ref_10","unstructured":"Du, Y., Wang, W., and Wang, L. (2015, January 7\u201312). Hierarchical recurrent neural network for skeleton based action recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, S., Li, W., Cook, C., Zhu, C., and Gao, Y. (2018, January 20\u201322). Independently recurrent neural network (IndRNN): Building a longer and deeper RNN. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00572"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., and Lin, D. (2018, January 2\u20137). Spatial temporal graph convolutional networks for skeleton-based action recognition. Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"120080","DOI":"10.1016\/j.eswa.2023.120080","article-title":"Efficient skeleton-based action recognition via multi-stream depthwise separable convolutional neural network","volume":"226","author":"Yin","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_14","unstructured":"Kipf, T., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Si, C., Chen, W., Wang, W., Wang, L., and Tan, T. (2019, January 16\u201320). An attention enhanced graph convolutional LSTM network for skeleton-based action recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00132"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., and Lu, H. (2019, January 16\u201320). Two-stream adaptive graph convolutional networks for skeleton-based action recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01230"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Song, Y., Zhang, Z., Shan, C., and Wang, L. (2020, January 12\u201316). Stronger, faster and more explainable: A graph convolutional baseline for skeleton-based action recognition. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA.","DOI":"10.1145\/3394171.3413802"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1474","DOI":"10.1109\/TPAMI.2022.3157033","article-title":"Constructing stronger and faster baselines for skeleton-based action recognition","volume":"45","author":"Song","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.neucom.2021.02.001","article-title":"Attention adjacency matrix based graph convolutional networks for skeleton-based action recognition","volume":"440","author":"Xie","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"41403","DOI":"10.1109\/ACCESS.2022.3164711","article-title":"Skeleton-based ST-GCN for human action recognition with extended skeleton graph and partitioning strategy","volume":"10","author":"Wang","year":"2022","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"9532","DOI":"10.1109\/TIP.2020.3028207","article-title":"Skeleton-based action recognition with multi-stream adaptive graph convolutional networks","volume":"29","author":"Shi","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shahroudy, A., Liu, J., Ng, T., and Wang, G. (2016, January 27\u201330). NTU RGB + D: A large scale dataset for 3D human activity analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.115"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2684","DOI":"10.1109\/TPAMI.2019.2916873","article-title":"NTU RGB + D 120: A large-scale benchmark for 3D human activity understandin","volume":"42","author":"Liu","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, T., Liu, J., Zhang, W., Ni, Y., Wang, W., and Li, Z. (2021, January 19\u201325). UAV-Human: A large benchmark for human behavior understanding with unmanned aerial vehicles. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01600"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1007\/s11263-012-0594-8","article-title":"Dense trajectories and motion boundary descriptors for action recognition","volume":"103","author":"Wang","year":"2013","journal-title":"Proc. Int. J. Comput. Vis."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Vemulapalli, R., Arrate, F., and Chellappa, R. (2014, January 23\u201328). Human action recognition by representing 3D skeletons as points in a lie group. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA.","DOI":"10.1109\/CVPR.2014.82"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fernando, B., Gavves, E., Oramas, M., Ghodrati, A., and Tuytelaars, T. (2015, January 20\u201326). Modeling video evolution for action recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA.","DOI":"10.1109\/CVPR.2015.7299176"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.patcog.2017.02.030","article-title":"Enhanced skeleton visualization for view invariant human action recognition","volume":"68","author":"Liu","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_29","unstructured":"Li, B., Dai, Y., Cheng, X., Chen, H., Lin, Y., and He, M. (2017, January 10\u201314). Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep CNN. Proceedings of the IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Brisbane, Australia."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Song, S., Lan, C., Xing, J., Zeng, W., and Liu, J. (2017, January 4\u20139). An end-to-end spatio-temporal attention model for human action recognition from skeleton data. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11212"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., and Zheng, N. (2017, January 21\u201326). View adaptive recurrent neural networks for high performance human action recognition from skeleton data. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.233"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., and Tian, Q. (2019, January 16\u201320). Actional structural graph convolutional networks for skeleton-based action recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00371"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Song, Y., Zhang, Z., Shan, C., and Wang, L. (2019, January 22\u201325). Richly activated graph convolutional network for robust skeleton-based action recognition. Proceedings of the IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8802917"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Nan, M., and Florea, A.M. (2022). Fast Temporal Graph Convolutional Model for Skeleton-Based Action Recognition. Sensors, 22.","DOI":"10.3390\/s22197117"},{"key":"ref_35","unstructured":"Simonyan, K., and Zisserman, A. (2014, January 8\u201311). Two-stream convolutional networks for action recognition in videos. Proceedings of the Conference and Workshop on Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Baradel, F., Wolf, C., and Mille, J. (2017, January 11\u201317). Human action recognition: Pose-based attention draws focus to hands. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCVW.2017.77"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Si, C., Jing, Y., Wang, W., Wang, L., and Tan, T. (2018, January 8\u201314). Skeleton-based action recognition with spatial reasoning and temporal stack learning. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01246-5_7"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yang, S., Wang, X., Gao, L., and Song, J. (2022, January 18\u201322). MKE-GCN: Multi-modal knowledge embedded graph convolutional network for skeletonbased action recognition in the wild. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Taipei, Taiwan.","DOI":"10.1109\/ICME52920.2022.9859787"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"109062","DOI":"10.1016\/j.sigpro.2023.109062","article-title":"Learning spatial-temporal feature with graph product","volume":"210","author":"Tan","year":"2023","journal-title":"Signal Process"},{"key":"ref_40","unstructured":"Li, T., Liu, J., Zhang, W., and Duan, L. (2022, January 24\u201328). Hard-net: Hardness-aware discrimination network for 3D early activity prediction. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel."},{"key":"ref_41","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (July, January 26). Learning Deep Features for Discriminative Localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6397\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:11:59Z","timestamp":1760127119000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6397"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,14]]},"references-count":41,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23146397"],"URL":"https:\/\/doi.org\/10.3390\/s23146397","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,14]]}}}