{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T07:02:54Z","timestamp":1773212574127,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T00:00:00Z","timestamp":1668729600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T00:00:00Z","timestamp":1668729600000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s11042-022-14214-y","type":"journal-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T23:03:04Z","timestamp":1668812584000},"page":"19829-19851","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep learning-based multi-view 3D-human action recognition using skeleton and depth data"],"prefix":"10.1007","volume":"82","author":[{"given":"Sampat Kumar","family":"Ghosh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2101-5992","authenticated-orcid":false,"given":"Rashmi","family":"M","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biju R","family":"Mohan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ram Mohana Reddy","family":"Guddeti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,18]]},"reference":[{"key":"14214_CR1","doi-asserted-by":"publisher","first-page":"104090","DOI":"10.1016\/j.imavis.2020.104090","volume":"106","author":"F Afza","year":"2021","unstructured":"Afza F, Khan MA, Sharif M, Kadry S, Manogaran G, Saba T et al (2021) A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection. Image Vis Comput 106:104090","journal-title":"Image Vis Comput"},{"issue":"3","key":"14214_CR2","doi-asserted-by":"publisher","first-page":"3623","DOI":"10.1109\/JSEN.2020.3028561","volume":"21","author":"Z Ahmad","year":"2021","unstructured":"Ahmad Z, Khan N (2021) CNN-based multistage gated average fusion (MGAF) for human action recognition using depth and inertial sensors. IEEE Sens J 21(3):3623\u20133634","journal-title":"IEEE Sens J"},{"key":"14214_CR3","doi-asserted-by":"crossref","unstructured":"Ben Tanfous A, Drira H, Ben AB (2018) Coding kendall\u2019s shape trajectories for 3d action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2840\u20132849","DOI":"10.1109\/CVPR.2018.00300"},{"issue":"1","key":"14214_CR4","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1080\/21645515.2017.1379639","volume":"14","author":"UA Bhatti","year":"2018","unstructured":"Bhatti UA, Huang M, Wang H, Zhang Y, Mehmood A, Di W (2018) Recommendation system for immunization coverage and monitoring. Human Vaccines & Immunotherapeutics 14(1):165\u2013171","journal-title":"Human Vaccines & Immunotherapeutics"},{"issue":"3","key":"14214_CR5","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1080\/17517575.2018.1557256","volume":"13","author":"UA Bhatti","year":"2019","unstructured":"Bhatti UA, Huang M, Wu D, Zhang Y, Mehmood A, Han H (2019) Recommendation system using feature extraction and pattern recognition in clinical care systems. Enterprise Information Systems 13(3):329\u2013351","journal-title":"Enterprise Information Systems"},{"key":"14214_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3090410","volume":"60","author":"UA Bhatti","year":"2021","unstructured":"Bhatti UA, Yu Z, Chanussot J, Zeeshan Z, Yuan L, Luo W et al (2021) Local Similarity-Based Spatial\u2013Spectral fusion hyperspectral image classification with deep CNN and gabor filtering. IEEE Trans Geosci Remote Sens 60:1\u201315","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"10","key":"14214_CR7","doi-asserted-by":"publisher","first-page":"14780","DOI":"10.1007\/s11356-021-16627-y","volume":"29","author":"UA Bhatti","year":"2022","unstructured":"Bhatti UA, Yu Z, Hasnain A, Nawaz SA, Yuan L, Wen L et al (2022) Evaluating the impact of roads on the diversity pattern and density of trees to improve the conservation of species. Environ Sci Pollut Res 29(10):14780\u201314790","journal-title":"Environ Sci Pollut Res"},{"key":"14214_CR8","doi-asserted-by":"publisher","first-page":"132569","DOI":"10.1016\/j.chemosphere.2021.132569","volume":"288","author":"UA Bhatti","year":"2022","unstructured":"Bhatti UA, Zeeshan Z, Nizamani MM, Bazai S, Yu Z, Yuan L (2022) Assessing the change of ambient air quality patterns in Jiangsu Province of China pre-to post-COVID-19. Chemosphere 288:132569","journal-title":"Chemosphere"},{"key":"14214_CR9","doi-asserted-by":"crossref","unstructured":"Canny J (1986) A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. (6):679\u2013698","DOI":"10.1109\/TPAMI.1986.4767851"},{"key":"14214_CR10","doi-asserted-by":"crossref","unstructured":"Chen C, Liu K, Jafari R, Kehtarnavaz N (2014) Home-based senior fitness test measurement system using collaborative inertial and depth sensors. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society. IEEE, pp 4135\u20134138","DOI":"10.1109\/EMBC.2014.6944534"},{"issue":"15","key":"14214_CR11","doi-asserted-by":"publisher","first-page":"1995","DOI":"10.1016\/j.patrec.2013.02.006","volume":"34","author":"L Chen","year":"2013","unstructured":"Chen L, Wei H, Ferryman J (2013) A survey of human motion analysis using depth imagery. Pattern Recogn Lett 34(15):1995\u20132006","journal-title":"Pattern Recogn Lett"},{"key":"14214_CR12","doi-asserted-by":"crossref","unstructured":"Dhiman C, Saxena M, Vishwakarma DK (2019) Skeleton-based view invariant deep features for human activity recognition. In: 2019 IEEE Fifth international conference on multimedia big data (BigMM). IEEE, pp 225\u2013230","DOI":"10.1109\/BigMM.2019.00-21"},{"key":"14214_CR13","doi-asserted-by":"publisher","first-page":"3835","DOI":"10.1109\/TIP.2020.2965299","volume":"29","author":"C Dhiman","year":"2020","unstructured":"Dhiman C, Vishwakarma DK (2020) View-invariant deep architecture for human action recognition using two-stream motion and shape temporal dynamics. IEEE Trans Image Process 29:3835\u20133844","journal-title":"IEEE Trans Image Process"},{"key":"14214_CR14","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.patcog.2017.12.004","volume":"77","author":"W Ding","year":"2018","unstructured":"Ding W, Liu K, Belyaev E, Cheng F (2018) Tensor-based linear dynamical systems for action recognition from 3D skeletons. Pattern Recogn 77:75\u201386","journal-title":"Pattern Recogn"},{"issue":"1","key":"14214_CR15","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1007\/s10489-020-01803-3","volume":"51","author":"C Ding","year":"2021","unstructured":"Ding C, Liu K, Cheng F, Belyaev E (2021) Spatio-temporal attention on manifold space for 3D human action recognition. Appl Intell 51(1):560\u2013570","journal-title":"Appl Intell"},{"key":"14214_CR16","doi-asserted-by":"publisher","first-page":"15280","DOI":"10.1109\/ACCESS.2020.2968054","volume":"8","author":"Y Fan","year":"2020","unstructured":"Fan Y, Weng S, Zhang Y, Shi B, Zhang Y (2020) Context-aware cross-attention for skeleton-based human action recognition. IEEE Access 8:15280\u201315290","journal-title":"IEEE Access"},{"key":"14214_CR17","doi-asserted-by":"crossref","unstructured":"Ghosh SK, Rashmi M, Mohan BR, Guddeti RMR (2022) Skeleton-based human action recognition using motion and orientation of joints. In: Advanced machine intelligence and signal processing. Springer, pp 75\u201386","DOI":"10.1007\/978-981-19-0840-8_6"},{"key":"14214_CR18","doi-asserted-by":"publisher","first-page":"103818","DOI":"10.1016\/j.imavis.2019.10.004","volume":"93","author":"Y Gu","year":"2020","unstructured":"Gu Y, Ye X, Sheng W, Ou Y, Li Y (2020) Multiple stream deep learning model for human action recognition. Image Vis Comput 93:103818","journal-title":"Image Vis Comput"},{"key":"14214_CR19","doi-asserted-by":"crossref","unstructured":"Hu JF, Zheng WS, Lai J, Zhang J (2015) Jointly learning heterogeneous features for RGB-D activity recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5344\u20135352","DOI":"10.1109\/CVPR.2015.7299172"},{"key":"14214_CR20","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.ins.2019.10.047","volume":"513","author":"T Huynh-The","year":"2020","unstructured":"Huynh-The T, Hua CH, Ngo TT, Kim DS (2020) Image representation of pose-transition feature for 3D skeleton-based action recognition. Inf Sci 513:112\u2013126","journal-title":"Inf Sci"},{"issue":"2","key":"14214_CR21","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1109\/LRA.2021.3059624","volume":"6","author":"MM Islam","year":"2021","unstructured":"Islam MM, Iqbal T (2021) Multi-gat: a graphical attention-based hierarchical multimodal representation learning approach for human activity recognition. IEEE Robotics and Automation Letters 6(2):1729\u20131736","journal-title":"IEEE Robotics and Automation Letters"},{"issue":"9","key":"14214_CR22","doi-asserted-by":"publisher","first-page":"1806","DOI":"10.1109\/TSMC.2018.2850149","volume":"49","author":"A Kamel","year":"2018","unstructured":"Kamel A, Sheng B, Yang P, Li P, Shen R, Feng DD (2018) Deep convolutional neural networks for human action recognition using depth maps and postures. IEEE Transactions on Systems, Man, and Cybernetics: Systems 49(9):1806\u20131819","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"issue":"1","key":"14214_CR23","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/s11063-020-10400-x","volume":"53","author":"R Kanjilal","year":"2021","unstructured":"Kanjilal R, Uysal I (2021) The future of human activity recognition: deep learning or feature engineering? Neur Process Lett 53(1):561\u2013579","journal-title":"Neur Process Lett"},{"key":"14214_CR24","doi-asserted-by":"crossref","unstructured":"Ke Q, Bennamoun M, An S, Sohel F, Boussaid F (2017) A new representation of skeleton sequences for 3d action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3288\u20133297","DOI":"10.1109\/CVPR.2017.486"},{"key":"14214_CR25","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv:14126980"},{"key":"14214_CR26","unstructured":"Li B, Camps OI, Sznaier M (2012) Cross-view activity recognition using hankelets. In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, pp 1362\u20131369"},{"key":"14214_CR27","unstructured":"Li R, Zickler T (2012) Discriminative virtual views for cross-view action recognition. In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, pp 2855\u20132862"},{"key":"14214_CR28","doi-asserted-by":"crossref","unstructured":"Liu H, Zhang L, Guan L, Liu M (2020) GFNEt: a lightweight group frame network for efficient human action recognition. In: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2583\u20132587","DOI":"10.1109\/ICASSP40776.2020.9053939"},{"key":"14214_CR29","unstructured":"Mahjoub AB, Atri M (2016) Human action recognition using RGB data. In: 2016 11th international Design & Test Symposium (IDT). IEEE, pp 83\u201387"},{"key":"14214_CR30","doi-asserted-by":"crossref","unstructured":"Maji S, Bourdev L, Malik J (2011) Action recognition from a distributed representation of pose and appearance. In: CVPR 2011. IEEE, pp 3177\u20133184","DOI":"10.1109\/CVPR.2011.5995631"},{"key":"14214_CR31","doi-asserted-by":"crossref","unstructured":"Megavannan V, Agarwal B, Babu RV (2012) Human action recognition using depth maps. In: 2012 international conference on signal processing and communications (SPCOM). IEEE, pp 1\u20135","DOI":"10.1109\/SPCOM.2012.6290032"},{"key":"14214_CR32","doi-asserted-by":"crossref","unstructured":"Oreifej O, Liu Z (2013) Hon4d: histogram of oriented 4d normals for activity recognition from depth sequences. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 716\u2013723","DOI":"10.1109\/CVPR.2013.98"},{"issue":"3","key":"14214_CR33","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1049\/iet-cvi.2018.5014","volume":"13","author":"HH Pham","year":"2018","unstructured":"Pham HH, Khoudour L, Crouzil A, Zegers P, Velastin SA (2018) Learning to recognise 3D human action from a new skeleton-based representation using deep convolutional neural networks. IET Comput Vision 13(3):319\u2013328","journal-title":"IET Comput Vision"},{"issue":"12","key":"14214_CR34","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 Trans Pattern Anal Mach Intell 38(12):2430\u20132443","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"14214_CR35","doi-asserted-by":"crossref","unstructured":"Rahmani H, Mian A (2016) 3d action recognition from novel viewpoints. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1506\u20131515","DOI":"10.1109\/CVPR.2016.167"},{"key":"14214_CR36","doi-asserted-by":"crossref","unstructured":"Romaissa BD, Mourad O, Brahim N (2021) Vision-based multi-modal framework for action recognition. In: 2020 25th international conference on pattern recognition (ICPR). IEEE, pp 5859\u20135866","DOI":"10.1109\/ICPR48806.2021.9412863"},{"key":"14214_CR37","doi-asserted-by":"crossref","unstructured":"Shahroudy A, Liu J, Ng TT, Wang G (2016) Ntu rgb+ d: a large scale dataset for 3d human activity analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1010\u20131019","DOI":"10.1109\/CVPR.2016.115"},{"issue":"5","key":"14214_CR38","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1109\/TPAMI.2017.2691321","volume":"40","author":"A Shahroudy","year":"2017","unstructured":"Shahroudy A, Ng TT, Gong Y, Wang G (2017) Deep multimodal feature analysis for action recognition in rgb+ d videos. IEEE Trans Pattern Anal Mach Intell 40(5):1045\u20131058","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"14214_CR39","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1109\/TCSVT.2020.2965574","volume":"31","author":"Z Shao","year":"2021","unstructured":"Shao Z, Li Y, Zhang H (2021) Learning representations from skeletal self-similarities for cross-view action recognition. IEEE Trans Circuits Syst Video Technol 31(1):160\u2013174","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"14214_CR40","doi-asserted-by":"crossref","unstructured":"Singh R, Khurana R, Kushwaha AKS, Srivastava R (2020) Combining CNN streams of dynamic image and depth data for action recognition. Multimedia Systems 1\u201310","DOI":"10.1007\/s00530-019-00645-5"},{"key":"14214_CR41","doi-asserted-by":"crossref","unstructured":"Song S, Lan C, Xing J, Zeng W, Liu J (2017) An end-to-end spatio-temporal attention model for human action recognition from skeleton data. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1)","DOI":"10.1609\/aaai.v31i1.11212"},{"key":"14214_CR42","doi-asserted-by":"crossref","unstructured":"Sun Z, Ke Q, Rahmani H, Bennamoun M, Wang G, Liu J (2022) Human action recognition from various data modalities: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 1\u201320","DOI":"10.1109\/TPAMI.2022.3183112"},{"key":"14214_CR43","unstructured":"Wang P, Li W, Gao Z, Zhang J, Tang C, Ogunbona P (2015) Deep convolutional neural networks for action recognition using depth map sequences. arXiv:150104686"},{"issue":"5","key":"14214_CR44","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 (2013) Learning actionlet ensemble for 3D human action recognition. IEEE Trans Pattern Anal Mach Intell 36 (5):914\u2013927","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"14214_CR45","doi-asserted-by":"crossref","unstructured":"Wang J, Nie X, Xia Y, Wu Y, Zhu SC (2014) Cross-view action modeling, learning and recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2649\u20132656","DOI":"10.1109\/CVPR.2014.339"},{"key":"14214_CR46","doi-asserted-by":"crossref","unstructured":"Wei P, Zheng N, Zhao Y, Zhu SC (2013) Concurrent action detection with structural prediction. In: Proceedings of the IEEE international conference on computer vision, pp 3136\u20133143","DOI":"10.1109\/ICCV.2013.389"},{"key":"14214_CR47","doi-asserted-by":"crossref","unstructured":"Xia L, Aggarwal J (2013) Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2834\u20132841","DOI":"10.1109\/CVPR.2013.365"},{"key":"14214_CR48","doi-asserted-by":"crossref","unstructured":"Yan S, Xiong Y, Lin D (2018) Spatial temporal graph convolutional networks for skeleton-based action recognition. Proceedings of the AAAI Conference On Artificial Intelligence. 32(1)","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"14214_CR49","doi-asserted-by":"crossref","unstructured":"Yang X, Tian Y (2014) Super normal vector for activity recognition using depth sequences. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 804\u2013811","DOI":"10.1109\/CVPR.2014.108"},{"issue":"2","key":"14214_CR50","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/MMUL.2012.24","volume":"19","author":"Z Zhang","year":"2012","unstructured":"Zhang Z (2012) Microsoft kinect sensor and its effect. IEEE Multimedia 19(2):4\u201310","journal-title":"IEEE Multimedia"},{"key":"14214_CR51","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1109\/TIP.2019.2937724","volume":"29","author":"P Zhang","year":"2019","unstructured":"Zhang P, Xue J, Lan C, Zeng W, Gao Z, Zheng N (2019) Eleatt-rnn: adding attentiveness to neurons in recurrent neural networks. IEEE Trans Image Process 29:1061\u20131073","journal-title":"IEEE Trans Image Process"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-14214-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-14214-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-14214-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,22]],"date-time":"2023-04-22T04:09:02Z","timestamp":1682136542000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-14214-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,18]]},"references-count":51,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["14214"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-14214-y","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,18]]},"assertion":[{"value":"5 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We have declared no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}