{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T09:01:41Z","timestamp":1725872501905},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319488950"},{"type":"electronic","value":"9783319488967"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-48896-7_72","type":"book-chapter","created":{"date-parts":[[2016,11,26]],"date-time":"2016-11-26T14:18:05Z","timestamp":1480169885000},"page":"723-737","source":"Crossref","is-referenced-by-count":0,"title":["Semi-supervised Learning for Human Pose Recognition with RGB-D Light-Model"],"prefix":"10.1007","author":[{"given":"Xinbo","family":"Wang","sequence":"first","affiliation":[]},{"given":"Guoshan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Dahai","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Dan","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,11,27]]},"reference":[{"key":"72_CR1","doi-asserted-by":"crossref","unstructured":"Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: 17th International Conference on Proceedings of the Pattern Recognition (ICPR 2004), vol. 3, pp. 32\u201336. IEEE Computer Society (2004)","DOI":"10.1109\/ICPR.2004.1334462"},{"key":"72_CR2","unstructured":"Heng, W., Schmid, C.: Action recognition with improved trajectories. In: 2013 IEEE International Conference on Computer Vision (ICCV). IEEE (2013)"},{"key":"72_CR3","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)"},{"key":"72_CR4","doi-asserted-by":"crossref","unstructured":"Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACM Multimedia, vol. 2 (2014)","DOI":"10.1145\/2647868.2654889"},{"key":"72_CR5","doi-asserted-by":"crossref","unstructured":"Ch\u00e9ron, G., Laptev, I., Schmid, C.: P-CNN: pose-based CNN features for action recognition. In: Proceedings of the IEEE International Conference on Computer Vision (2015)","DOI":"10.1109\/ICCV.2015.368"},{"key":"72_CR6","doi-asserted-by":"crossref","unstructured":"Tran, D., et al.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"72_CR7","doi-asserted-by":"crossref","unstructured":"Karpathy, A., et al.: Large-scale video classification with convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2014)","DOI":"10.1109\/CVPR.2014.223"},{"issue":"1","key":"72_CR8","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1145\/2398356.2398381","volume":"56","author":"J Shotton","year":"2013","unstructured":"Shotton, J., et al.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116\u2013124 (2013)","journal-title":"Commun. ACM"},{"key":"72_CR9","unstructured":"Newcombe, R.A., et al.: KinectFusion: real-time dense surface mapping and tracking. In: 2013 IEEE International Conference on Computer Vision (ICCV). IEEE (2013)"},{"key":"72_CR10","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)","DOI":"10.1109\/CVPR.2015.7298631"},{"key":"72_CR11","unstructured":"Lu, X., Aggarwal, J.K.: Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2013)"},{"key":"72_CR12","doi-asserted-by":"crossref","unstructured":"Oreifej, O., Liu, Z.: HON4D: histogram of oriented 4d normals for activity recognition from depth sequences. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2013)","DOI":"10.1109\/CVPR.2013.98"},{"key":"72_CR13","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems (2014)"},{"key":"72_CR14","unstructured":"Ng, J.Y.-H., et al.: Beyond short snippets: deep networks for video classification. arXiv preprint arXiv:1503.08909a (2015)"},{"key":"72_CR15","doi-asserted-by":"crossref","unstructured":"Wang, K., Wang, X., Lin, L., et al.: 3D human activity recognition with reconfigurable convolutional neural networks. In: Proceedings of the ACM International Conference on Multimedia. ACM (2014)","DOI":"10.1145\/2647868.2654912"},{"key":"72_CR16","unstructured":"Whelan, T., et al.: Kintinuous: spatially extended kinectfusion. MIT-CSAIL-TR-2012-020 (2012)"},{"key":"72_CR17","doi-asserted-by":"crossref","unstructured":"Nie\u00dfner, M., et al.: Real-time 3d reconstruction at scale using voxel hashing. ACM Trans. Graph. (TOG) 32(6) (2013). Article No. 169","DOI":"10.1145\/2508363.2508374"},{"key":"72_CR18","doi-asserted-by":"crossref","unstructured":"Whelan, T., et al.: ElasticFusion: dense SLAM without a pose graph. In: RSS (2015)","DOI":"10.15607\/RSS.2015.XI.001"},{"key":"72_CR19","doi-asserted-by":"crossref","unstructured":"Blan, A.O., et al.: Shining a light on human pose: on shadows, shading and the estimation of pose and shape. In: IEEE 11th International Conference on Computer Vision, ICCV 2007. IEEE (2007)","DOI":"10.1109\/ICCV.2007.4409005"},{"key":"72_CR20","doi-asserted-by":"crossref","unstructured":"Lee, M.W., Nevatia, R.: Body part detection for human pose estimation and tracking. In: IEEE Workshop on Motion and Video Computing, WMVC 2007. IEEE (2007)","DOI":"10.1109\/WMVC.2007.10"},{"key":"72_CR21","doi-asserted-by":"crossref","unstructured":"Lee, M.W., Nevatia, R.: Dynamic human pose estimation using Markov chain Monte Carlo approach. In: Seventh IEEE Workshops on Application of Computer Vision, WACV\/MOTIONS 2005, vol. 1\u20132. IEEE (2005)","DOI":"10.1109\/ACVMOT.2005.43"},{"key":"72_CR22","doi-asserted-by":"crossref","unstructured":"Fathi, A., Mori, G.: Human pose estimation using motion exemplars. In: IEEE 11th International Conference on Computer Vision, ICCV 2007. IEEE (2007)","DOI":"10.1109\/ICCV.2007.4409073"},{"key":"72_CR23","doi-asserted-by":"crossref","unstructured":"Baumberg, A.M., Hogg, D.C.: An efficient method for contour tracking using active shape models. In: Proceedings of the 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects. IEEE (1994)","DOI":"10.1109\/MNRAO.1994.346236"},{"key":"72_CR24","doi-asserted-by":"crossref","unstructured":"Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3d points. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE (2010)","DOI":"10.1109\/CVPRW.2010.5543273"},{"key":"72_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1007\/978-3-642-33275-3_31","volume-title":"Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications","author":"AW Vieira","year":"2012","unstructured":"Vieira, A.W., Nascimento, E.R., Oliveira, G.L., Liu, Z., Campos, M.F.M.: STOP: Space-Time Occupancy Patterns for 3D action recognition from depth map sequences. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds.) CIARP 2012. LNCS, vol. 7441, pp. 252\u2013259. Springer, Heidelberg (2012). doi: 10.1007\/978-3-642-33275-3_31"},{"key":"72_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"872","DOI":"10.1007\/978-3-642-33709-3_62","volume-title":"12th European Conference on Computer Vision","author":"J Wang","year":"2012","unstructured":"Wang, J., Liu, Z., Chorowski, J., Chen, Z., Wu, Y.: Robust 3d action recognition with random occupancy patterns. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7441, pp. 872\u2013885. Springer, Heidelberg (2012). doi: 10.1007\/978-3-642-33709-3_62"},{"key":"72_CR27","unstructured":"Mao, Y., et al.: Accurate 3d pose estimation from a single depth image. In: 2011 IEEE International Conference on Computer Vision (ICCV). IEEE (2011)"},{"key":"72_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1007\/978-3-642-18421-5_11","volume-title":"Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging","author":"A Criminisi","year":"2011","unstructured":"Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression forests for efficient anatomy detection and localization in CT studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MCV 2010. LNCS, vol. 6533, pp. 106\u2013117. Springer, Heidelberg (2011). doi: 10.1007\/978-3-642-18421-5_11"},{"issue":"1","key":"72_CR29","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1177\/1420326X11423163","volume":"21","author":"A Jalal","year":"2011","unstructured":"Jalal, A., et al.: Recognition of human home activities via depth silhouettes and transformation for smart homes. Indoor Built Environ. 21(1), 184\u2013190 (2011)","journal-title":"Indoor Built Environ."},{"key":"72_CR30","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhang, C., Tian, Y.: Recognizing actions using depth motion maps-based histograms of oriented gradients. In: Proceedings of the 20th ACM International Conference on Multimedia. ACM (2012)","DOI":"10.1145\/2393347.2396382"},{"key":"72_CR31","unstructured":"Wu, S.-L., Cui, R.-Y.: Human behavior recognition based on sitting postures. In: 2010 International Symposium on Computer Communication Control and Automation (3CA), vol. 1. IEEE (2010)"},{"key":"72_CR32","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"72_CR33","doi-asserted-by":"crossref","unstructured":"Wang, X., Gupta, A.: Unsupervised learning of visual representations using videos. arXiv preprint arXiv:1505.00687 (2015)","DOI":"10.1109\/ICCV.2015.320"},{"key":"72_CR34","doi-asserted-by":"crossref","unstructured":"Stikic, M., Van Laerhoven, K., Schiele, B.: Exploring semi-supervised and active learning for activity recognition. In: 12th IEEE International Symposium on Wearable Computers, ISWC 2008. IEEE (2008)","DOI":"10.1109\/ISWC.2008.4911590"},{"key":"72_CR35","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.neucom.2012.04.038","volume":"105","author":"X Zhao","year":"2013","unstructured":"Zhao, X., et al.: Human action recognition based on semi-supervised discriminant analysis with global constraint. Neurocomputing 105, 45\u201350 (2013)","journal-title":"Neurocomputing"},{"issue":"10","key":"72_CR36","doi-asserted-by":"crossref","first-page":"2334","DOI":"10.1016\/j.patcog.2010.06.018","volume":"44","author":"T Zhang","year":"2011","unstructured":"Zhang, T., et al.: Boosted multi-class semi-supervised learning for human action recognition. Pattern Recogn. 44(10), 2334\u20132342 (2011)","journal-title":"Pattern Recogn."},{"key":"72_CR37","doi-asserted-by":"crossref","unstructured":"Guan, D., et al.: Activity recognition based on semi-supervised learning. In: 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2007. IEEE (2007)","DOI":"10.1109\/RTCSA.2007.17"},{"key":"72_CR38","unstructured":"Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc.: Ser. B (Methodol.) 39, 1\u201338 (1977)"},{"key":"72_CR39","unstructured":"Miller, D.J., Uyar, H.S.: A mixture of experts classifier with learning based on both labelled and unlabelled data. In: Advances in Neural Information Processing Systems (1997)"},{"key":"72_CR40","unstructured":"Zhao, Y., et al.: Combing RGB and depth map features for human activity recognition. In: 2012 Asia-Pacific on Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE (2012)"},{"key":"72_CR41","doi-asserted-by":"crossref","unstructured":"Faria, D.R., Premebida, C., Nunes, U.: A probabilistic approach for human everyday activities recognition using body motion from RGB-D images. In: 2014 RO-MAN: The 23rd IEEE International Symposium on Robot and Human Interactive Communication. IEEE (2014)","DOI":"10.1109\/ROMAN.2014.6926340"},{"key":"72_CR42","doi-asserted-by":"crossref","unstructured":"Ming, Y., Ruan, Q., Hauptmann, A.G.: Activity recognition from RGB-D camera with 3d local spatio-temporal features. In: 2012 IEEE International Conference on Multimedia and Expo (ICME). IEEE (2012)","DOI":"10.1109\/ICME.2012.8"}],"container-title":["Lecture Notes in Computer Science","Advances in Multimedia Information Processing - PCM 2016"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-48896-7_72","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,16]],"date-time":"2019-09-16T00:27:53Z","timestamp":1568593673000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-48896-7_72"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319488950","9783319488967"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-48896-7_72","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]}}}