{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:08:11Z","timestamp":1742972891056,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":19,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811674426"},{"type":"electronic","value":"9789811674433"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-7443-3_4","type":"book-chapter","created":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T17:54:45Z","timestamp":1636480485000},"page":"47-58","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A New RGB-D Gesture Video Dataset and Its Benchmark Evaluations on Light-Weighted Networks"],"prefix":"10.1007","author":[{"given":"Guojian","family":"Xiao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhendong","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhirong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panji","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianping","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,10]]},"reference":[{"unstructured":"Chen, Z.L., Lin, X.L., Chen, J.Y., Huang, Q.Y., Li, C.: Research and application of gesture recognition and rehabilitation system based on computer vision. Comput. Meas. Control 7, 203\u2013207 (2021)","key":"4_CR1"},{"key":"4_CR2","first-page":"181","volume":"44","author":"BY Su","year":"2013","unstructured":"Su, B.Y., Wang, G.J., Zhang, J.: Smart home system based on internet of things and kinect sensor. J. Cent. South Univ. (Sci. Technol.) 44, 181\u2013184 (2013)","journal-title":"J. Cent. South Univ. (Sci. Technol.)"},{"doi-asserted-by":"crossref","unstructured":"Sha, J., Ma, J., Mou, H.J., Hou, J.H.: A review of vision based dynamic hand gestures recognition. Comput. Sci. Appl. 10, 990\u20131001 (2020)","key":"4_CR3","DOI":"10.12677\/CSA.2020.105102"},{"unstructured":"Zhao, Q.N.: Research on gesture recognition technology based on computer vision. Dalian University of Technology (2020)","key":"4_CR4"},{"unstructured":"Zhou, S.: Gesture recognition based on feature fusion: Zhengzhou University (2019)","key":"4_CR5"},{"unstructured":"Li, J.M.: 3D hand gesture recognition in RGBD images. Graduate School of National University of Defense Technology (2017)","key":"4_CR6"},{"unstructured":"Kang, C.Q.: Hand gesture recognition and application based on RGBD Data. Chang\u2019an University (2017)","key":"4_CR7"},{"key":"4_CR8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.eswa.2018.03.063","volume":"106","author":"F Negin","year":"2018","unstructured":"Negin, F., et al.: PRAXIS: towards automatic cognitive assessment using gesture recognition. Expert Syst. Appl. 106, 21\u201335 (2018)","journal-title":"Expert Syst. Appl."},{"doi-asserted-by":"crossref","unstructured":"Zhang, Y.F., Cao, C., Cheng, J., Lu, H.: EgoGesture: a new dataset and benchmark for egocentric hand gesture recognition. IEEE Trans. Multimed. 20(5), 1038\u20131050 (2018)","key":"4_CR9","DOI":"10.1109\/TMM.2018.2808769"},{"doi-asserted-by":"crossref","unstructured":"Chai, X.X., Wang, H., Yin, F., Chen, X.: Communication tool for the hard of hearings: a large vocabulary sign language recognition system. In: 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 781\u2013783 (2015)","key":"4_CR10","DOI":"10.1109\/ACII.2015.7344659"},{"doi-asserted-by":"crossref","unstructured":"Lin, F., Wilhelm, C., Martinez, T.: Two-hand global 3D pose estimation using monocular RGB. In: The CVF Winter Conference on Applications of Computer Vision (WACV), pp. 2373\u20132381 (2021)","key":"4_CR11","DOI":"10.1109\/WACV48630.2021.00242"},{"doi-asserted-by":"crossref","unstructured":"Wan, J., Li, S.Z., Zhao, Y., Shuai, Z., Escalera, S.: ChaLearn looking at people RGB-D isolated and continuous datasets for gesture recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 761\u2013769 (2016)","key":"4_CR12","DOI":"10.1109\/CVPRW.2016.100"},{"doi-asserted-by":"crossref","unstructured":"Yuan, S.H., Jordi, S.R., Taking, L., Kai, L.H., Wen, H.C.: LaRED: a large RGB-D extensible hand gesture dataset. In: Xu, C.S. (ed.) Multimedia Systems Conference, pp. 53\u201328 (2014)","key":"4_CR13","DOI":"10.1145\/2557642.2563669"},{"key":"4_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2629500","volume":"33","author":"J Tompson","year":"2014","unstructured":"Tompson, J., Stein, M., Lecun, Y., Perlin, K.: Real-Time continuous pose recovery of human hands using convolutional networks. ACM Trans. Graph. 33, 1\u201310 (2014)","journal-title":"ACM Trans. Graph."},{"unstructured":"Qin, F.: Real-time dynamic gesture recognition based on deep learning. Zhejiang University (2020)","key":"4_CR15"},{"unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv (2017)","key":"4_CR16"},{"doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6848\u20136856 (2018)","key":"4_CR17","DOI":"10.1109\/CVPR.2018.00716"},{"doi-asserted-by":"crossref","unstructured":"Escalera, S., Baro, X., Escalante, H.J., Guyon, I.: ChaLearn looking at people: a review of events and resources. In: Choe, Y. (ed.) 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1594\u20131601 (2017)","key":"4_CR18","DOI":"10.1109\/IJCNN.2017.7966041"},{"doi-asserted-by":"crossref","unstructured":"Kopuklu, O., Kose, N., Gunduz, A., Rigoll, G.: Resource efficient 3D convolutional neural networks. In: IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 1910\u20131919 (2019)","key":"4_CR19","DOI":"10.1109\/ICCVW.2019.00240"}],"container-title":["Communications in Computer and Information Science","Theoretical Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-7443-3_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T21:02:20Z","timestamp":1726088540000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-7443-3_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811674426","9789811674433"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-7443-3_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NCTCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"National Conference of Theoretical Computer Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Yinchuan","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"39","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nctcs2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conf.ccf.org.cn\/TCS2021","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CCF Consys","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"145","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":"67","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":"46% - 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","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":"5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}