{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:28:27Z","timestamp":1742974107870,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030341190"},{"type":"electronic","value":"9783030341206"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-34120-6_8","type":"book-chapter","created":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T17:03:47Z","timestamp":1574874227000},"page":"93-102","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Hierarchical Graph Convolutional Network for Skeleton-Based Action Recognition"],"prefix":"10.1007","author":[{"given":"Linjiang","family":"Huang","sequence":"first","affiliation":[]},{"given":"Yan","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Wanli","family":"Ouyang","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,28]]},"reference":[{"key":"8_CR1","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, pp. 3844\u20133852 (2016)"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Du, Y., Fu, Y., Wang, L.: Skeleton based action recognition with convolutional neural network. In: Asian Conference on Pattern Recognition, pp. 579\u2013583. IEEE (2015)","DOI":"10.1109\/ACPR.2015.7486569"},{"key":"8_CR3","doi-asserted-by":"publisher","first-page":"3010","DOI":"10.1109\/TIP.2016.2552404","volume":"25","author":"Y Du","year":"2016","unstructured":"Du, Y., Fu, Y., Wang, L.: Representation learning of temporal dynamics for skeleton-based action recognition. IEEE Trans. Image Process. 25, 3010\u20133022 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"8_CR4","unstructured":"Du, Y., Wang, W., Wang, L.: Hierarchical recurrent neural network for skeleton based action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1110\u20131118. IEEE (2015)"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Kim, T.S., Reiter, A.: Interpretable 3d human action analysis with temporal convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1623\u20131631. IEEE (2017)","DOI":"10.1109\/CVPRW.2017.207"},{"key":"8_CR6","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"8_CR7","unstructured":"Li, C., Zhong, Q., Xie, D., Pu, S.: Skeleton-based action recognition with convolutional neural networks. In: IEEE International Conference on Multimedia & Expo Workshops, pp. 597\u2013600. IEEE (2017)"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Li, C., Zhong, Q., Xie, D., Pu, S.: Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation. In: International Joint Conference on Artificial Intelligence, pp. 786\u2013792 (2018)","DOI":"10.24963\/ijcai.2018\/109"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Li, C., Cui, Z., Zheng, W., Xu, C., Yang, J.: Spatio-temporal graph convolution for skeleton based action recognition. arXiv preprint arXiv:1802.09834 (2018)","DOI":"10.1609\/aaai.v32i1.11776"},{"key":"8_CR10","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.patcog.2017.02.030","volume":"68","author":"M Liu","year":"2017","unstructured":"Liu, M., Liu, H., Chen, C.: Enhanced skeleton visualization for view invariant human action recognition. Pattern Recogn. 68, 346\u2013362 (2017)","journal-title":"Pattern Recogn."},{"key":"8_CR11","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1016\/j.imavis.2009.11.014","volume":"28","author":"R Poppe","year":"2010","unstructured":"Poppe, R.: A survey on vision-based human action recognition. Image Vis. Comput. 28, 976\u2013990 (2010)","journal-title":"Image Vis. Comput."},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Shahroudy, A., Liu, J., Ng, T.T., Wang, G.: Ntu rgb+d: A large scale dataset for 3d human activity analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1010\u20131019. IEEE (2016)","DOI":"10.1109\/CVPR.2016.115"},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Shotton, J., et al.: Real-time human pose recognition in parts from single depth images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1297\u20131304. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995316"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Si, C., Jing, Y., Wang, W., Wang, L., Tan, T.: Skeleton-based action recognition with spatial reasoning and temporal stack learning. arXiv preprint arXiv:1805.02335 (2018)","DOI":"10.1007\/978-3-030-01246-5_7"},{"key":"8_CR15","unstructured":"Thakkar, K., Narayanan, P.: Part-based graph convolutional network for action recognition. arXiv preprint arXiv:1809.04983 (2018)"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Vemulapalli, R., Arrate, F., Chellappa, R.: Human action recognition by representing 3d skeletons as points in a lie group. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 588\u2013595. IEEE (2014)","DOI":"10.1109\/CVPR.2014.82"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, L.: Modeling temporal dynamics and spatial configurations of actions using two-stream recurrent neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 499\u2013508. IEEE (2017)","DOI":"10.1109\/CVPR.2017.387"},{"key":"8_CR18","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1109\/TPAMI.2013.198","volume":"36","author":"J Wang","year":"2014","unstructured":"Wang, J., Liu, Z., Wu, Y., Yuan, J.: Learning actionlet ensemble for 3d human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36, 914\u2013927 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Wang, J., Nie, X., Xia, Y., Wu, Y., Zhu, S.C.: Cross-view action modeling, learning and recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2649\u20132656. IEEE (2014)","DOI":"10.1109\/CVPR.2014.339"},{"key":"8_CR20","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.cviu.2018.04.007","volume":"171","author":"P Wang","year":"2017","unstructured":"Wang, P., Li, W., Ogunbona, P., Wan, J., Escalera, S.: Rgb-d-based human motion recognition with deep learning: a survey. Comput. Vis. Image Understand. 171, 118\u2013139 (2017)","journal-title":"Comput. Vis. Image Understand."},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Xia, L., Chen, C.C., Aggarwal, J.K.: View invariant human action recognition using histograms of 3d joints. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 20\u201327. IEEE (2012)","DOI":"10.1109\/CVPRW.2012.6239233"},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. arXiv preprint arXiv:1801.07455 (2018)","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"8_CR23","unstructured":"Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. In: Advances in Neural Information Processing Systems, pp. 4801\u20134811 (2018)"},{"key":"8_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, S., Liu, X., Xiao, J.: On geometric features for skeleton-based action recognition using multilayer lstm networks. In: IEEE Winter Conference on Applications of Computer Vision, pp. 148\u2013157. IEEE (2017)","DOI":"10.1109\/WACV.2017.24"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-34120-6_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:16:59Z","timestamp":1693527419000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-34120-6_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030341190","9783030341206"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-34120-6_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"28 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.csig.org.cn\/detail\/2669","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}