{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T20:07:25Z","timestamp":1768075645651,"version":"3.49.0"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T00:00:00Z","timestamp":1565049600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T00:00:00Z","timestamp":1565049600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP160101458"],"award-info":[{"award-number":["DP160101458"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2019,10]]},"DOI":"10.1007\/s11263-019-01192-2","type":"journal-article","created":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T14:44:33Z","timestamp":1565102673000},"page":"1545-1564","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Learning Human Pose Models from Synthesized Data for Robust RGB-D Action Recognition"],"prefix":"10.1007","volume":"127","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3258-0380","authenticated-orcid":false,"given":"Jian","family":"Liu","sequence":"first","affiliation":[]},{"given":"Hossein","family":"Rahmani","sequence":"additional","affiliation":[]},{"given":"Naveed","family":"Akhtar","sequence":"additional","affiliation":[]},{"given":"Ajmal","family":"Mian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,6]]},"reference":[{"key":"1192_CR1","doi-asserted-by":"crossref","unstructured":"Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE conference on computer vision and pattern recognition (Vol. 1, pp. 886\u2013893).","DOI":"10.1109\/CVPR.2005.177"},{"key":"1192_CR2","doi-asserted-by":"crossref","unstructured":"Dalal, N., Triggs, B., & Schmid, C. (2006). Human detection using oriented histograms of flow and appearance. In European conference on computer vision (pp. 428\u2013441).","DOI":"10.1007\/11744047_33"},{"key":"1192_CR3","doi-asserted-by":"crossref","unstructured":"Donahue, J., Anne\u00a0Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., et al. (2015). Long-term recurrent convolutional networks for visual recognition and description. In IEEE conference on computer vision and pattern recognition (pp. 2625\u20132634).","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"1192_CR4","unstructured":"Du, Y., Wang, W., & Wang, L. (2015). Hierarchical recurrent neural network for skeletonbased action recognition. In IEEE conference on computer vision andpattern recognition (pp. 1110\u20131118)."},{"key":"1192_CR5","doi-asserted-by":"crossref","unstructured":"Evangelidis, G., Singh, G., & Horaud, R. (2014). Skeletal quads: Human action recognition using joint quadruples. In International conference on pattern recognition (pp. 4513\u20134518).","DOI":"10.1109\/ICPR.2014.772"},{"key":"1192_CR6","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan, R. E., Chang, K. W., Hsieh, C. J., Wang, X. R., & Lin, C. J. (2008). LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9, 1871\u20131874.","journal-title":"Journal of Machine Learning Research"},{"key":"1192_CR7","doi-asserted-by":"crossref","unstructured":"Farhadi, A., & Tabrizi, M. K. (2008). Learning to recognize activities from the wrong view point. In European conference on computer vision (pp. 154\u2013166).","DOI":"10.1007\/978-3-540-88682-2_13"},{"key":"1192_CR8","doi-asserted-by":"crossref","unstructured":"Farhadi, A., Tabrizi, M. K., Endres, I., & Forsyth, D. (2009). A latent model of discriminative aspect. In IEEE international conference on computer vision (pp. 948\u2013955).","DOI":"10.1109\/ICCV.2009.5459350"},{"key":"1192_CR9","doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C., Pinz, A., & Zisserman, A. (2016). Convolutional two-stream network fusion for video action recognition. In IEEE conference on computer vision and pattern recognition (pp. 1933\u20131941).","DOI":"10.1109\/CVPR.2016.213"},{"key":"1192_CR10","doi-asserted-by":"crossref","unstructured":"Gkioxari, G., & Malik, J. (2015). Finding action tubes. In IEEE conference on computer vision and pattern recognition (pp. 759\u2013768).","DOI":"10.1109\/CVPR.2015.7298676"},{"key":"1192_CR11","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672\u20132680)."},{"key":"1192_CR12","doi-asserted-by":"crossref","unstructured":"Gopalan, R., Li, R., & Chellappa, R. (2011). Domain adaptation for object recognition: An unsupervised approach. In IEEE international conference on computer vision (pp. 999\u20131006).","DOI":"10.1109\/ICCV.2011.6126344"},{"key":"1192_CR13","doi-asserted-by":"crossref","unstructured":"Gupta, A., Martinez, J., Little, J. J., & Woodham, R. J. (2014). 3D pose from motion for cross-view action recognition via non-linear circulant temporal encoding. In IEEE conference on computer vision and pattern recognition (pp. 2601\u20132608).","DOI":"10.1109\/CVPR.2014.333"},{"key":"1192_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016a). Deep residual learning for image recognition. In IEEE conference on computer vision and pattern recognition (pp. 770\u2013778).","DOI":"10.1109\/CVPR.2016.90"},{"key":"1192_CR15","doi-asserted-by":"crossref","unstructured":"He, Y., Shirakabe, S., Satoh, Y., & Kataoka, H. (2016b). Human action recognition without human. In European conference on computer vision workshops (pp. 11\u201317).","DOI":"10.1007\/978-3-319-49409-8_2"},{"key":"1192_CR16","doi-asserted-by":"crossref","unstructured":"Hu, J. F., Zheng, W. S., Lai, J., & Zhang, J. (2015). Jointly learning heterogeneous features for RGB-D activity recognition. In IEEE conference on computer vision and pattern recognition (pp. 5344\u20135352).","DOI":"10.1109\/CVPR.2015.7299172"},{"key":"1192_CR17","unstructured":"Huang, Z., Wan, C., Probst, T., & Van\u00a0Gool, L. (2016). Deep learning on lie groups for skeleton-based action recognition. In IEEE conference on computer vision and pattern recognition."},{"issue":"1","key":"1192_CR18","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2013","unstructured":"Ji, S., Xu, W., Yang, M., & Yu, K. (2013). 3D convolutional neural networks for human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1), 221\u2013231.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1192_CR19","doi-asserted-by":"crossref","unstructured":"Jia, C., Kong, Y., Ding, Z., Fu, Y. R. (2014a). Latent tensor transfer learning for RGB-D action recognition. In ACM international conference on multimedia (pp. 87\u201396).","DOI":"10.1145\/2647868.2654928"},{"key":"1192_CR20","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014b). Caffe: Convolutional architecture for fast feature embedding. \n                    arXiv:1408.5093\n                    \n                  .","DOI":"10.1145\/2647868.2654889"},{"key":"1192_CR21","doi-asserted-by":"crossref","unstructured":"Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L. (2014). Large-scale video classification with convolutional neural networks. In IEEE conference on computer vision and pattern recognition (pp. 1725\u20131732).","DOI":"10.1109\/CVPR.2014.223"},{"key":"1192_CR22","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.cviu.2016.10.004","volume":"154","author":"T Kerola","year":"2017","unstructured":"Kerola, T., Inoue, N., & Shinoda, K. (2017). Cross-view human action recognition from depth maps using spectral graph sequences. Computer Vision and Image Understanding, 154, 108\u2013126.","journal-title":"Computer Vision and Image Understanding"},{"issue":"3","key":"1192_CR23","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1007\/s11263-016-0982-6","volume":"123","author":"Y Kong","year":"2017","unstructured":"Kong, Y., & Fu, Y. (2017). Max-margin heterogeneous information machine for RGB-D action recognition. International Journal of Computer Vision, 123(3), 350\u2013371.","journal-title":"International Journal of Computer Vision"},{"key":"1192_CR24","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097\u20131105)."},{"key":"1192_CR25","unstructured":"Li, B., Camps, O. I., & Sznaier, M. (2012) Cross-view activity recognition using hankelets. In IEEE conference on computer vision and pattern recognition (pp. 1362\u20131369)."},{"key":"1192_CR26","unstructured":"Li, R., & Zickler, T. (2012). Discriminative virtual views for cross-view action recognition. In IEEE conference on computer vision and pattern recognition (pp. 2855\u20132862)."},{"key":"1192_CR27","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, W., Mahadevan, V., & Vasconcelos, N. (2016). VLAD3: Encoding dynamics of deep features for action recognition. In IEEE conference on computer vision and pattern recognition (pp. 1951\u20131960).","DOI":"10.1109\/CVPR.2016.215"},{"key":"1192_CR28","doi-asserted-by":"crossref","unstructured":"Liu, J., Shah, M., Kuipers, B., & Savarese, S. (2011). Cross-view action recognition via view knowledge transfer. In IEEE conference on computer vision and pattern recognition (pp. 3209\u20133216).","DOI":"10.1109\/CVPR.2011.5995729"},{"key":"1192_CR29","doi-asserted-by":"crossref","unstructured":"Liu, J., Shahroudy, A., Xu, D., & Wang, G. (2016). Spatio-temporal LSTM with trust gates for 3D human action recognition. In European conference on computer vision (pp. 816\u2013833).","DOI":"10.1007\/978-3-319-46487-9_50"},{"key":"1192_CR30","doi-asserted-by":"crossref","unstructured":"Luo, Z., Peng, B., Huang, D. A., Alahi, A., & Fei-Fei, L. (2017). Unsupervised learning of long-term motion dynamics for videos. In IEEE conference on computer vision and pattern recognition.","DOI":"10.1007\/978-3-319-42999-1"},{"key":"1192_CR31","doi-asserted-by":"crossref","unstructured":"Lv, F., & Nevatia, R. (2007). Single view human action recognition using key pose matching and viterbi path searching. In IEEE conference on computer vision and pattern recognition (pp. 1\u20138).","DOI":"10.1109\/CVPR.2007.383131"},{"key":"1192_CR32","doi-asserted-by":"publisher","first-page":"205","DOI":"10.21105\/joss.00205","volume":"2","author":"L McInnes","year":"2017","unstructured":"McInnes, L., Healy, J., & Astels, S. (2017). HDBSCAN: Hierarchical density based clustering. The Journal of Open Source Software, 2, 205.","journal-title":"The Journal of Open Source Software"},{"key":"1192_CR33","doi-asserted-by":"crossref","unstructured":"Ohn-Bar, E., & Trivedi, M. (2013). Joint angles similarities and HOG2 for action recognition. In IEEE conference on computer vision and pattern recognition workshops (pp. 465\u2013470).","DOI":"10.1109\/CVPRW.2013.76"},{"key":"1192_CR34","doi-asserted-by":"crossref","unstructured":"Oreifej, O., & Liu, Z. (2013). HON4D: Histogram of oriented 4D normals for activity recognition from depth sequences. In IEEE conference on computer vision and pattern recognition (pp. 716\u2013723).","DOI":"10.1109\/CVPR.2013.98"},{"issue":"66","key":"1192_CR35","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s11263-005-3671-4","volume":"1","author":"V Parameswaran","year":"2006","unstructured":"Parameswaran, V., & Chellappa, R. (2006). View invariance for human action recognition. International Journal of Computer Vision, 1(66), 83\u2013101.","journal-title":"International Journal of Computer Vision"},{"key":"1192_CR36","doi-asserted-by":"crossref","unstructured":"Pfister, T., Charles, J., & Zisserman, A. (2015). Flowing convnets for human pose estimation in videos. In IEEE international conference on computer vision (pp. 1913\u20131921).","DOI":"10.1109\/ICCV.2015.222"},{"key":"1192_CR37","doi-asserted-by":"crossref","unstructured":"Rahmani, H., & Mian, A. (2015). Learning a non-linear knowledge transfer model for cross-view action recognition. In IEEE conference on computer vision and pattern recognition (pp. 2458\u20132466).","DOI":"10.1109\/CVPR.2015.7298860"},{"key":"1192_CR38","doi-asserted-by":"crossref","unstructured":"Rahmani, H., & Mian, A. (2016). 3d action recognition from novel viewpoints. In IEEE conference on computer vision and pattern recognition (pp. 1506\u20131515).","DOI":"10.1109\/CVPR.2016.167"},{"issue":"12","key":"1192_CR39","doi-asserted-by":"publisher","first-page":"2430","DOI":"10.1109\/TPAMI.2016.2533389","volume":"38","author":"H Rahmani","year":"2016","unstructured":"Rahmani, H., Mahmood, A., Huynh, D., & Mian, A. (2016). Histogram of oriented principal components for cross-view action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(12), 2430\u20132443.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1192_CR40","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1109\/TPAMI.2017.2691768","volume":"40","author":"H Rahmani","year":"2017","unstructured":"Rahmani, H., Mian, A., & Shah, M. (2017). Learning a deep model for human action recognition from novel viewpoints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 667\u2013681.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"2","key":"1192_CR41","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1023\/A:1020350100748","volume":"50","author":"C Rao","year":"2002","unstructured":"Rao, C., Yilmaz, A., & Shah, M. (2002). View-invariant representation and recognition of actions. International Journal of Computer Vision, 50(2), 203\u2013226.","journal-title":"International Journal of Computer Vision"},{"key":"1192_CR42","doi-asserted-by":"crossref","unstructured":"Shahroudy, A., Liu, J., Ng, T. T., & Wang, G. (2016a). NTU RGB+D: A large scale dataset for 3d human activity analysis. In IEEE conference on computer vision and pattern recognition (pp. 1010\u20131019).","DOI":"10.1109\/CVPR.2016.115"},{"issue":"10","key":"1192_CR43","doi-asserted-by":"publisher","first-page":"2123","DOI":"10.1109\/TPAMI.2015.2505295","volume":"38","author":"A Shahroudy","year":"2016","unstructured":"Shahroudy, A., Ng, T. T., Yang, Q., & Wang, G. (2016b). Multimodal multipart learning for action recognition in depth videos. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(10), 2123\u20132129.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1192_CR44","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1109\/TPAMI.2017.2691321","volume":"40","author":"A Shahroudy","year":"2017","unstructured":"Shahroudy, A., Ng, T. T., Gong, Y., & Wang, G. (2017). Deep multimodal feature analysis for action recognition in RGB+D videos. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 1045\u20131058.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1192_CR45","unstructured":"Shakhnarovich, G. (2005). Learning task-specific similarity. Ph.D. thesis, Massachusetts Institute of Technology."},{"key":"1192_CR46","unstructured":"Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R. (2016). Learning from simulated and unsupervised images through adversarial training. \n                    arXiv:1612.07828\n                    \n                  ."},{"key":"1192_CR47","unstructured":"Simonyan, K., & Zisserman, A. (2014). Two-stream convolutional networks for action recognition in videos. In Advances in neural information processing systems (pp. 568\u2013576)."},{"key":"1192_CR48","unstructured":"Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. \n                    arXiv:1212.0402\n                    \n                  ."},{"key":"1192_CR49","doi-asserted-by":"crossref","unstructured":"Su, B., Zhou, J., Ding, X., Wang, H., & Wu, Y. (2016). Hierarchical dynamic parsing and encoding for action recognition. In European conference on computer vision (pp. 202\u2013217).","DOI":"10.1007\/978-3-319-46493-0_13"},{"key":"1192_CR50","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. (2015). Going deeper with convolutions. In IEEE conference on computer vision and pattern recognition (pp. 1\u20139).","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1192_CR51","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015). Learning spatiotemporal features with 3D convolutional networks. In IEEE international conference on computer vision (pp. 4489\u20134497).","DOI":"10.1109\/ICCV.2015.510"},{"key":"1192_CR52","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1109\/TPAMI.2017.2712608","volume":"40","author":"G Varol","year":"2017","unstructured":"Varol, G., Laptev, I., & Schmid, C. (2017a). Long-term temporal convolutions for action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 1510\u20131517.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1192_CR53","doi-asserted-by":"crossref","unstructured":"Varol, G., Romero, J., Martin, X., Mahmood, N., Black, M. J., Laptev, I., et al. (2017b). Learning from Synthetic Humans. In IEEE conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2017.492"},{"key":"1192_CR54","doi-asserted-by":"crossref","unstructured":"Vemulapalli, R., Arrate, F., & Chellappa, R. (2014). 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).","DOI":"10.1109\/CVPR.2014.82"},{"key":"1192_CR55","doi-asserted-by":"crossref","unstructured":"Wang, H., & Schmid, C. (2013). Action recognition with improved trajectories. In IEEE international conference on computer vision (pp. 3551\u20133558).","DOI":"10.1109\/ICCV.2013.441"},{"key":"1192_CR56","doi-asserted-by":"crossref","unstructured":"Wang, H., Kl\u00e4ser, A., Schmid, C., & Liu, C. L. (2011). Action recognition by dense trajectories. In IEEE conference on computer vision and pattern recognition (pp. 3169\u20133176).","DOI":"10.1109\/CVPR.2011.5995407"},{"issue":"1","key":"1192_CR57","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1007\/s11263-012-0594-8","volume":"103","author":"H Wang","year":"2013","unstructured":"Wang, H., Kl\u00e4ser, A., Schmid, C., & Liu, C. L. (2013a). Dense trajectories and motion boundary descriptors for action recognition. International Journal of Computer Vision, 103(1), 60\u201379.","journal-title":"International Journal of Computer Vision"},{"key":"1192_CR58","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1109\/TPAMI.2013.198","volume":"36","author":"J Wang","year":"2013","unstructured":"Wang, J., Liu, Z., Wu, Y., & Yuan, J. (2013b). Learning actionlet ensemble for 3D human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 914\u2013927.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1192_CR59","doi-asserted-by":"crossref","unstructured":"Wang, J., Nie, X., Xia, Y., Wu, Y., & Zhu, S. C. (2014). Cross-view action modeling, learning and recognition. In IEEE conference on computer vision and pattern recognition (pp. 2649\u20132656).","DOI":"10.1109\/CVPR.2014.339"},{"key":"1192_CR60","doi-asserted-by":"crossref","unstructured":"Wang, L., Qiao, Y., & Tang, X. (2015). Action recognition with trajectory-pooled deep-convolutional descriptors. In IEEE conference on computer vision and pattern recognition (pp. 4305\u20134314).","DOI":"10.1109\/CVPR.2015.7299059"},{"key":"1192_CR61","doi-asserted-by":"crossref","unstructured":"Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., et al. (2016a). Temporal segment networks: Towards good practices for deep action recognition. In European conference on computer vision (pp. 20\u201336).","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"1192_CR62","doi-asserted-by":"crossref","unstructured":"Wang, P., Li, Z., Hou, Y., & Li, W. (2016b). Action recognition based on joint trajectory maps using convolutional neural networks. In ACM on multimedia conference (pp. 102\u2013106).","DOI":"10.1145\/2964284.2967191"},{"key":"1192_CR63","doi-asserted-by":"crossref","unstructured":"Wang, Y., & Hoai, M. (2016). Improving human action recognition by non-action classification. In IEEE conference on computer vision and pattern recognition (pp. 2698\u20132707).","DOI":"10.1109\/CVPR.2016.295"},{"issue":"2","key":"1192_CR64","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.cviu.2006.07.013","volume":"104","author":"D Weinland","year":"2006","unstructured":"Weinland, D., Ronfard, R., & Boyer, E. (2006). Free viewpoint action recognition using motion history volumes. Computer Vision and Image Understanding, 104(2), 249\u2013257.","journal-title":"Computer Vision and Image Understanding"},{"key":"1192_CR65","doi-asserted-by":"crossref","unstructured":"Weinland, D., Boyer, E., & Ronfard, R. (2007). Action recognition from arbitrary views using 3D exemplars. In IEEE international conference on computer vision (pp. 1\u20137).","DOI":"10.1109\/ICCV.2007.4408849"},{"key":"1192_CR66","doi-asserted-by":"crossref","unstructured":"Yang, X., & Tian, Y. (2014). Super normal vector for activity recognition using depth sequences. In IEEE conference on computer vision and pattern recognition (pp. 804\u2013811).","DOI":"10.1109\/CVPR.2014.108"},{"key":"1192_CR67","doi-asserted-by":"crossref","unstructured":"Yilmaz, A., & Shah, M. (2005). Actions sketch: A novel action representation. In IEEE conference on computer vision and pattern recognition (Vol. 1, pp. 984\u2013989).","DOI":"10.1109\/CVPR.2005.58"},{"key":"1192_CR68","unstructured":"Yu, F., Zhang, Y., Song, S., Seff, A., & Xiao, J. (2015). LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop. CoRR."},{"issue":"8","key":"1192_CR69","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.1109\/TPAMI.2015.2491925","volume":"38","author":"M Yu","year":"2016","unstructured":"Yu, M., Liu, L., & Shao, L. (2016). Structure-preserving binary representations for RGB-D action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(8), 1651\u20131664.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1192_CR70","doi-asserted-by":"crossref","unstructured":"Zhang, B., Wang, L., Wang, Z., Qiao, Y., & Wang, H. (2016). Real-time action recognition with enhanced motion vector CNNs. In IEEE conference on computer vision and pattern recognition (pp. 2718\u20132726).","DOI":"10.1109\/CVPR.2016.297"},{"key":"1192_CR71","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, C., Xiao, B., Zhou, W., Liu, S., & Shi, C. (2013). Cross-view action recognition via a continuous virtual path. In IEEE conference on computer vision and pattern recognition (pp. 2690\u20132697).","DOI":"10.1109\/CVPR.2013.347"},{"key":"1192_CR72","doi-asserted-by":"crossref","unstructured":"Zheng, J., & Jiang, Z. (2013). Learning view-invariant sparse representations for cross-view action recognition. In IEEE international conference on computer vision (pp. 3176\u20133183).","DOI":"10.1109\/ICCV.2013.394"},{"key":"1192_CR73","doi-asserted-by":"crossref","unstructured":"Zhu, W., Hu, J., Sun, G., Cao, X., & Qiao, Y. (2016). A key volume mining deep framework for action recognition. In IEEE conference on computer vision and pattern recognition (pp. 1991\u20131999).","DOI":"10.1109\/CVPR.2016.219"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-019-01192-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11263-019-01192-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-019-01192-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T23:13:06Z","timestamp":1596582786000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11263-019-01192-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,6]]},"references-count":73,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2019,10]]}},"alternative-id":["1192"],"URL":"https:\/\/doi.org\/10.1007\/s11263-019-01192-2","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,6]]},"assertion":[{"value":"3 July 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}