{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:43:24Z","timestamp":1742982204020,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031205026"},{"type":"electronic","value":"9783031205033"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-20503-3_51","type":"book-chapter","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T12:09:06Z","timestamp":1671192546000},"page":"570-581","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["3D Human Pose Estimation Based on\u00a0Multi-feature Extraction"],"prefix":"10.1007","author":[{"given":"Senlin","family":"Ge","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanming","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huitao","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,17]]},"reference":[{"key":"51_CR1","doi-asserted-by":"crossref","unstructured":"Bridgeman, L., Volino. M., Guillemaut, J.Y., et al.: Multi-person 3D pose estimation and tracking in sports. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE (2019)","DOI":"10.1109\/CVPRW.2019.00304"},{"key":"51_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/978-3-030-58452-8_12","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Tu","year":"2020","unstructured":"Tu, H., Wang, C., Zeng, W.: VoxelPose: towards multi-camera 3D human pose estimation in wild environment. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 197\u2013212. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_12"},{"key":"51_CR3","unstructured":"Zhe, C., Simon, T., Wei, S.E., et al.: Realtime multi-person 2D pose estimation using part affinity fields. IEEE (2017)"},{"key":"51_CR4","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., et al.: Mask R-CNN. IEEE Trans. Pattern Anal. Mach. Intell. (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"51_CR5","doi-asserted-by":"crossref","unstructured":"Joo, H., Simon, T., Li, X., et al.: Panoptic studio: a massively multiview system for social interaction capture. IEEE Trans. Pattern Anal. Mach. Intell. 99 (2016)","DOI":"10.1109\/ICCV.2015.381"},{"key":"51_CR6","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1109\/TPAMI.2015.2509986","volume":"38","author":"V Belagiannis","year":"2016","unstructured":"Belagiannis, V., Sikandar, A., et al.: 3D pictorial structures revisited: multiple human pose estimation. IEEE Trans. Pattern Anal. Mach. Intell. 38, 1929\u20131942 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"51_CR7","doi-asserted-by":"crossref","unstructured":"Qiu, H., Wang, C., Wang, J., et al.: Cross view fusion for 3D human pose estimation. University of Science and Technology of China; Microsoft Research Asia; TuSimple; Microsoft Research (2019)","DOI":"10.1109\/ICCV.2019.00444"},{"issue":"7","key":"51_CR8","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1109\/TPAMI.2013.248","volume":"36","author":"C Ionescu","year":"2014","unstructured":"Ionescu, C., Papava, D., Olaru, V., et al.: Human3.6M: large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1325\u20131339 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"51_CR9","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, C., Qiu, W., et al.: AdaFuse: adaptive multiview fusion for accurate human pose estimation in the wild. arXiv e-prints (2020)","DOI":"10.1007\/s11263-020-01398-9"},{"key":"51_CR10","unstructured":"Oberweger, M., Wohlhart, P., Lepetit, V.: DeepPose: human pose estimation via deep neural networks"},{"key":"51_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-3-319-46484-8_29","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Newell","year":"2016","unstructured":"Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483\u2013499. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_29"},{"key":"51_CR12","doi-asserted-by":"crossref","unstructured":"Huang, S., Gong, M., Tao, D.: A coarse-fine network for keypoint localization. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE (2017)","DOI":"10.1109\/ICCV.2017.329"},{"key":"51_CR13","doi-asserted-by":"crossref","unstructured":"Carreira, J., Agrawal, P., Fragkiadaki, K., et al.: Human pose estimation with iterative error feedback. IEEE (2015)","DOI":"10.1109\/CVPR.2016.512"},{"key":"51_CR14","doi-asserted-by":"crossref","unstructured":"Ke, L., Chang, M.C., Qi, H., et al.: Multi-scale structure-aware network for human pose estimation (2018)","DOI":"10.1109\/ICIP.2018.8451114"},{"key":"51_CR15","doi-asserted-by":"crossref","unstructured":"Fang, H.S., Xie, S., Tai, Y.W., et al.: RMPE: Regional Multi-person Pose Estimation. IEEE (2017)","DOI":"10.1109\/ICCV.2017.256"},{"key":"51_CR16","unstructured":"Chen, Y., Wang, Z., Peng, Y., et al.: Cascaded pyramid network for multi-person pose estimation"},{"key":"51_CR17","doi-asserted-by":"crossref","unstructured":"Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. arXiv e-prints (2018)","DOI":"10.1007\/978-3-030-01231-1_29"},{"key":"51_CR18","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, C., Zhu, H., et al.: CrowdPose: efficient crowded scenes pose estimation and a new benchmark (2018)","DOI":"10.1109\/CVPR.2019.01112"},{"key":"51_CR19","doi-asserted-by":"crossref","unstructured":"Pishchulin, L., Insafutdinov, E., Tang, S., et al.: DeepCut: joint subset partition and labeling for multi person pose estimation. IEEE (2016)","DOI":"10.1109\/CVPR.2016.533"},{"key":"51_CR20","doi-asserted-by":"crossref","unstructured":"Insafutdinov, E., Pishchulin, L., Andres, B., et al.: DeeperCut: a deeper, stronger, and faster multi-person pose estimation model. arXiv e-prints (2016)","DOI":"10.1007\/978-3-319-46466-4_3"},{"key":"51_CR21","doi-asserted-by":"crossref","unstructured":"Amin S, Andriluka M, Rohrbach M, et al. Multi-view Pictorial Structures for 3D Human Pose Estimation[C]\/\/ British Machine Vision Conference 2013. 2013","DOI":"10.5244\/C.27.45"},{"key":"51_CR22","doi-asserted-by":"crossref","unstructured":"Wang, C., Wang, Y., Lin, Z., et al.: Robust estimation of 3D human poses from a single image. arXiv e-prints (2014)","DOI":"10.1109\/CVPR.2014.303"},{"key":"51_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/978-3-642-33765-9_41","volume-title":"Computer Vision \u2013 ECCV 2012","author":"V Ramakrishna","year":"2012","unstructured":"Ramakrishna, V., Kanade, T., Sheikh, Y.: Reconstructing 3D human pose from 2D image landmarks. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 573\u2013586. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33765-9_41"},{"key":"51_CR24","doi-asserted-by":"crossref","unstructured":"Iskakov, K., Burkov, E., Lempitsky, V., et al.: Learnable triangulation of human pose. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV). IEEE (2020)","DOI":"10.1109\/ICCV.2019.00781"},{"key":"51_CR25","doi-asserted-by":"crossref","unstructured":"Lin, J., Lee, G.H.: Multi-view multi-person 3D pose estimation with plane sweep stereo (2021)","DOI":"10.1109\/CVPR46437.2021.01171"},{"key":"51_CR26","doi-asserted-by":"crossref","unstructured":"Chen, C.H., Ramanan, D.: 3D human pose estimation = 2D pose estimation + matching. IEEE (2017)","DOI":"10.1109\/CVPR.2017.610"},{"key":"51_CR27","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., Zhou, X., Derpanis, K.G., et al.: Coarse-to-fine volumetric prediction for single-image 3D human pose. In: IEEE Conference on Computer Vision & Pattern Recognition. IEEE (2017)","DOI":"10.1109\/CVPR.2017.139"},{"key":"51_CR28","doi-asserted-by":"crossref","unstructured":"Martinez, J., Hossain, R., Romero, J., et al.: A simple yet effective baseline for 3D human pose estimation. IEEE Computer Society (2017)","DOI":"10.1109\/ICCV.2017.288"},{"key":"51_CR29","doi-asserted-by":"crossref","unstructured":"Andriluka, M., Pishchulin, L., Gehler, P., et al.: Human pose estimation: new benchmark and state of the art analysis. In: Computer Vision and Pattern Recognition (CVPR). IEEE (2014)","DOI":"10.1109\/CVPR.2014.471"},{"key":"51_CR30","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., Zhou, X., Derpanis, K.G., et al.: Harvesting multiple views for marker-less 3D human pose annotations. IEEE (2017)","DOI":"10.1109\/CVPR.2017.138"},{"key":"51_CR31","doi-asserted-by":"crossref","unstructured":"Tome, D., Toso, M., Agapito, L., et al.: Rethinking pose in 3D: multi-stage refinement and recovery for markerless motion capture. IEEE (2018)","DOI":"10.1109\/3DV.2018.00061"},{"key":"51_CR32","doi-asserted-by":"crossref","unstructured":"Gordon, B., Raab, S., Azov, G., et al.: FLEX: parameter-free multi-view 3D human motion reconstruction (2021)","DOI":"10.1007\/978-3-031-19827-4_11"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20503-3_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T12:38:00Z","timestamp":1671194280000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20503-3_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031205026","9783031205033"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20503-3_51","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"472","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"164","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.1","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}