{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T17:08:11Z","timestamp":1783962491627,"version":"3.55.0"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Project of Guangdong Science and Technology Department (CN)","award":["2020B010166005"],"award-info":[{"award-number":["2020B010166005"]}]},{"name":"Project of Guangdong Science and Technology Department (CN)","award":["GDKTP2021032500"],"award-info":[{"award-number":["GDKTP2021032500"]}]},{"name":"Project of Guangdong Science and Technology Department (CN)","award":["22YJC630167"],"award-info":[{"award-number":["22YJC630167"]}]},{"name":"Project of Guangdong Science and Technology Department (CN)","award":["Z000158"],"award-info":[{"award-number":["Z000158"]}]},{"name":"Fund project of Department of Science and Technology of Guangdong Province","award":["2020B010166005"],"award-info":[{"award-number":["2020B010166005"]}]},{"name":"Fund project of Department of Science and Technology of Guangdong Province","award":["GDKTP2021032500"],"award-info":[{"award-number":["GDKTP2021032500"]}]},{"name":"Fund project of Department of Science and Technology of Guangdong Province","award":["22YJC630167"],"award-info":[{"award-number":["22YJC630167"]}]},{"name":"Fund project of Department of Science and Technology of Guangdong Province","award":["Z000158"],"award-info":[{"award-number":["Z000158"]}]},{"name":"Ministry of Education Social Science Fund","award":["2020B010166005"],"award-info":[{"award-number":["2020B010166005"]}]},{"name":"Ministry of Education Social Science Fund","award":["GDKTP2021032500"],"award-info":[{"award-number":["GDKTP2021032500"]}]},{"name":"Ministry of Education Social Science Fund","award":["22YJC630167"],"award-info":[{"award-number":["22YJC630167"]}]},{"name":"Ministry of Education Social Science Fund","award":["Z000158"],"award-info":[{"award-number":["Z000158"]}]},{"name":"Post-Doctoral Research Project","award":["2020B010166005"],"award-info":[{"award-number":["2020B010166005"]}]},{"name":"Post-Doctoral Research Project","award":["GDKTP2021032500"],"award-info":[{"award-number":["GDKTP2021032500"]}]},{"name":"Post-Doctoral Research Project","award":["22YJC630167"],"award-info":[{"award-number":["22YJC630167"]}]},{"name":"Post-Doctoral Research Project","award":["Z000158"],"award-info":[{"award-number":["Z000158"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Aiming at the problem that the features extracted from the original C3D (Convolutional 3D) convolutional neural network(C3D) were insufficient, and it was difficult to focus on keyframes, which led to the low accuracy of basketball players\u2019 action recognition; hence, a basketball action recognition method of deep neural network based on dynamic residual attention mechanism was proposed. Firstly, the traditional C3D is improved to a dynamic residual convolution network to extract sufficient feature information. Secondly, the extracted feature information is selected by the improved attention mechanism to obtain the key video frames. Finally, the algorithm is compared with the traditional C3D in order to demonstrate the advance and applicability of the algorithm. Experimental results show that this method can effectively recognize basketball posture, and the average accuracy of posture recognition is more than 97%.<\/jats:p>","DOI":"10.3390\/info14010013","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T05:28:24Z","timestamp":1672205304000},"page":"13","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Basketball Action Recognition Method of Deep Neural Network Based on Dynamic Residual Attention Mechanism"],"prefix":"10.3390","volume":"14","author":[{"given":"Jiongen","family":"Xiao","sequence":"first","affiliation":[{"name":"International Business School, Guangdong University of Finance and Economics, Guangzhou 510320, China"},{"name":"Electronic Forensics Laboratory, Guangzhou Institute of Software Application Technology, Guangzhou 511458, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8922-8962","authenticated-orcid":false,"given":"Wenchun","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Guangzhou Nanfang College, Guangzhou 510970, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Ding","sequence":"additional","affiliation":[{"name":"Electronic Forensics Laboratory, Guangzhou Institute of Software Application Technology, Guangzhou 511458, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109216","DOI":"10.1016\/j.patcog.2022.109216","article-title":"Hyper-sausage coverage function neuron model and learning algorithm for image classification","volume":"136","author":"Ning","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_2","first-page":"7668425","article-title":"Research on the Recognition Algorithm of Basketball Technical Action Based on BP Neural System","volume":"2022","author":"Hou","year":"2022","journal-title":"Sci. Program."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"110595","DOI":"10.1016\/j.measurement.2021.110595","article-title":"Hybrid lightweight Deep-learning model for Sensor-fusion basketball Shooting-posture recognition","volume":"189","author":"Fan","year":"2022","journal-title":"Measurement"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5562954","DOI":"10.1155\/2021\/5562954","article-title":"Application of motion sensor based on neural network in basketball technology and physical fitness evaluation system","volume":"2021","author":"Yuan","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7483536","DOI":"10.1155\/2016\/7483536","article-title":"Sports motion recognition using MCMR features based on interclass symbolic distance","volume":"12","author":"Wei","year":"2016","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13640-019-0415-x","article-title":"Automatic detection technology of sports athletes based on image recognition technology","volume":"2019","author":"Li","year":"2019","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wu, G., He, F., Zhou, Y., Jing, Y., Ning, X., Wang, C., and Jin, B. (2022). ACGAN: Age-compensated makeup transfer based on homologous continuity generative adversarial network model. IET Comput. Vis.","DOI":"10.1049\/cvi2.12138"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Song, Z., Zhao, X., Hui, Y., and Jiang, H. (2022). Fusing Attention Network based on Dilated Convolution for Super Resolution. IEEE Trans. Cogn. Dev. Syst.","DOI":"10.1109\/TCDS.2022.3153090"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhao, W., Wang, S., Wang, X., Zhao, Y., Li, T., Lin, J., and Wei, J. (2020, January 2). CZ-Base: A Database for Hand Gesture Recognition in Chinese Zither Intelligence Education. Proceedings of the International Forum on Digital TV and Wireless Multimedia Communications, Shanghai, China.","DOI":"10.1007\/978-981-16-1194-0_25"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"19545","DOI":"10.1007\/s11227-022-04649-3","article-title":"A time sequence location method of long video violence based on improved C3D network","volume":"78","author":"Qu","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"28821","DOI":"10.1109\/ACCESS.2022.3157823","article-title":"Fast 3D Visualization of Massive Geological Data Based on Clustering Index Fusion","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7790","DOI":"10.1109\/TGRS.2020.3038212","article-title":"Attention-aware pseudo-3-D convolutional neural network for hyperspectral image classification","volume":"59","author":"Lin","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., and Van Gool, L. (2016, January 11\u201314). Temporal segment networks: Towards good practices for deep action recognition. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13640-020-00501-x","article-title":"Improved two-stream model for human action recognition","volume":"2020","author":"Zhao","year":"2020","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fan, Y., Lu, X., Li, D., and Liu, Y. (2016, January 12\u201316). Video-based emotion recognition using CNN-RNN and C3D hybrid networks. Proceedings of the 18th ACM International Conference on Multimodal Interaction, Tokyo, Japan.","DOI":"10.1145\/2993148.2997632"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2956","DOI":"10.1109\/TCSVT.2017.2749509","article-title":"Large-scale gesture recognition with a fusion of RGB-D data based on saliency theory and C3D model","volume":"28","author":"Li","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"012138","DOI":"10.1088\/1742-6596\/2029\/1\/012138","article-title":"A review of action recognition based on convolutional neural network","volume":"1827","author":"Yang","year":"2021","journal-title":"J. Phys. Conf. Series. IOP Publ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, H., Das, A., and Saenko, K. (2017, January 22\u201329). R-c3d: Region convolutional 3d network for temporal activity detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.617"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"De Melo, W.C., Granger, E., and Hadid, A. (2019, January 14\u201318). Combining global and local convolutional 3d networks for detecting depression from facial expressions. Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), Lille, France.","DOI":"10.1109\/FG.2019.8756568"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., and Darrell, T. (2015, January 7\u201312). Long-term recurrent convolutional networks for visual recognition and description. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"ref_21","unstructured":"Tran, D., Ray, J., Shou, Z., Chang, S.F., and Paluri, M. (2017). Convnet architecture search for spatiotemporal feature learning. arXiv."},{"key":"ref_22","unstructured":"Qiu, Z., Yao, T., and Mei, T. (, January 22\u201329). Learning spatio-temporal representation with pseudo-3d residual networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Sun, X., Zha, Z.J., and Zeng, W. (2018, January 18\u201323). Mict: Mixed 3d\/2d convolutional tube for human action recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00054"},{"key":"ref_24","unstructured":"Simonyan, K., and Zisserman, A. (2014, January 8\u201313). Two-stream convolutional networks for action recognition in videos. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C., Pinz, A., and Zisserman, A. (2016, January 27\u201330). Convolutional two-stream network fusion for video action recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.213"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, B., Wang, L., Wang, Z., Qiao, Y., and Wang, H. (2016, January 27\u201330). Real-time action recognition with enhanced motion vector CNNs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.297"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.patrec.2018.05.018","article-title":"A review of convolutional-neural-network-based action recognition","volume":"118","author":"Yao","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_28","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u201312). Two-stream convolutional networks for action recognition. Proceedings of the Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Su, J.N. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.neucom.2020.06.032","article-title":"Human action recognition using convolutional LSTM and fully-connected LSTM with different attentions","volume":"410","author":"Zhang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_31","first-page":"1429042","article-title":"Injuries in college basketball sports based on machine learning from the perspective of the integration of sports and medicine","volume":"2022","author":"Zhao","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, Y., Chakraborty, R., and Yu, S.X. (2020, January 13\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/1\/13\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:52:42Z","timestamp":1760147562000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/1\/13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,27]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["info14010013"],"URL":"https:\/\/doi.org\/10.3390\/info14010013","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,27]]}}}