{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T07:41:48Z","timestamp":1775979708254,"version":"3.50.1"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,9,6]],"date-time":"2020-09-06T00:00:00Z","timestamp":1599350400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,6]],"date-time":"2020-09-06T00:00:00Z","timestamp":1599350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Advanced intelligent surveillance systems are able to automatically analyze video of surveillance data without human intervention. These systems allow high accuracy of human activity recognition and then a high-level activity evaluation. To provide such features, an intelligent surveillance system requires a background subtraction scheme for human segmentation that captures a sequence of images containing moving humans from the reference background image. This paper proposes an alternative approach for human segmentation in videos through the use of a deep convolutional neural network. Two specific datasets were created to train our network, using the shapes of 35 different moving actors arranged on background images related to the area where the camera is located, allowing the network to take advantage of the entire site chosen for video surveillance. To assess the proposed approach, we compare our results with an Adobe Photoshop tool called Select Subject, the conditional generative adversarial network Pix2Pix, and the fully-convolutional model for real-time instance segmentation Yolact. The results show that the main benefit of our method is the possibility to automatically recognize and segment people in videos without constraints on camera and people movements in the scene (Video, code and datasets are available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/graphics.unibas.it\/www\/HumanSegmentation\/index.md.html\">http:\/\/graphics.unibas.it\/www\/HumanSegmentation\/index.md.html<\/jats:ext-link>).<\/jats:p>","DOI":"10.1007\/s11042-020-09425-0","type":"journal-article","created":{"date-parts":[[2020,9,6]],"date-time":"2020-09-06T10:02:58Z","timestamp":1599386578000},"page":"1175-1199","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Human segmentation in surveillance video with deep learning"],"prefix":"10.1007","volume":"80","author":[{"given":"Monica","family":"Gruosso","sequence":"first","affiliation":[]},{"given":"Nicola","family":"Capece","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2942-7131","authenticated-orcid":false,"given":"Ugo","family":"Erra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,6]]},"reference":[{"issue":"16","key":"9425_CR1","doi-asserted-by":"publisher","first-page":"20415","DOI":"10.1007\/s11042-017-5438-7","volume":"77","author":"Q Abbas","year":"2018","unstructured":"Abbas Q, Ibrahim ME, Jaffar MA (2018) Video scene analysis: an overview and challenges on deep learning algorithms. Multimed Tools Appl 77 (16):20415\u201320453","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"9425_CR2","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1109\/JBHI.2018.2818620","volume":"23","author":"M Anthimopoulos","year":"2018","unstructured":"Anthimopoulos M, Christodoulidis S, Ebner L, Geiser T, Christe A, Mougiakakou S (2018) Semantic segmentation of pathological lung tissue with dilated fully convolutional networks. IEEE J Biomed Health Inform 23(2):714\u2013722","journal-title":"IEEE J Biomed Health Inform"},{"issue":"12","key":"9425_CR3","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"9425_CR4","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1111\/j.1467-8659.2011.02078.x","volume":"31","author":"F Banterle","year":"2012","unstructured":"Banterle F, Corsini M, Cignoni P, Scopigno R (2012) A low-memory, straightforward and fast bilateral filter through subsampling in spatial domain. Comput Graph Forum 31(1):19\u201332","journal-title":"Comput Graph Forum"},{"issue":"5","key":"9425_CR5","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1109\/TMI.2008.2010437","volume":"28","author":"KJ Batenburg","year":"2009","unstructured":"Batenburg KJ, Sijbers J (2009) Optimal threshold selection for tomogram segmentation by projection distance minimization. IEEE Trans Med Imaging 28(5):676\u2013686","journal-title":"IEEE Trans Med Imaging"},{"key":"9425_CR6","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.imavis.2016.04.007","volume":"51","author":"C Bhole","year":"2016","unstructured":"Bhole C, Pal C (2016) Fully automatic person segmentation in unconstrained video using spatio-temporal conditional random fields. Image Vis Comput 51:58\u201368","journal-title":"Image Vis Comput"},{"key":"9425_CR7","unstructured":"Bishop CM (2006) Pattern Recognition and Machine Learning. Springer http:\/\/research.microsoft.com\/en-us\/um\/people\/cmbishop\/prml\/"},{"key":"9425_CR8","doi-asserted-by":"crossref","unstructured":"Bolya D, Zhou C, Xiao F, Lee YJ (2019) Yolact: real-time instance segmentation. In: Proceedings of the IEEE international conference on computer vision, pp 9157\u20139166","DOI":"10.1109\/ICCV.2019.00925"},{"key":"9425_CR9","first-page":"28","volume":"77","author":"N Capece","year":"2019","unstructured":"Capece N, Banterle F, Cignoni P, Ganovelli F, Scopigno R, Erra U (2019) Deepflash: turning a flash selfie into a studio portrait. Signal Process: Image Commun 77:28\u201339","journal-title":"Signal Process: Image Commun"},{"issue":"9","key":"9425_CR10","doi-asserted-by":"publisher","first-page":"2175","DOI":"10.1109\/TPAMI.2013.18","volume":"35","author":"Q Chen","year":"2013","unstructured":"Chen Q, Li D, Tang CK (2013) Knn matting. IEEE Trans Pattern Anal Mach Intell 35(9):2175\u20132188","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9425_CR11","doi-asserted-by":"crossref","unstructured":"Chen X, Zou D, Zhiying Zhou S, Zhao Q, Tan P (2013) Image matting with local and nonlocal smooth priors. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1902\u20131907","DOI":"10.1109\/CVPR.2013.248"},{"key":"9425_CR12","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2014) Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv:1412.7062"},{"key":"9425_CR13","unstructured":"Dosovitskiy A, Springenberg JT, Riedmiller M, Brox T (2014) Discriminative unsupervised feature learning with convolutional neural networks. In: Proceedings of the 27th international conference on neural information processing systems, vol 1. NIPS\u201914. MIT Press, Cambridge, pp 766\u2013774. http:\/\/dl.acm.org\/citation.cfm?id=2968826.2968912"},{"issue":"3","key":"9425_CR14","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1145\/566654.566574","volume":"21","author":"F Durand","year":"2002","unstructured":"Durand F, Dorsey J (2002) Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans Graph 21(3):257\u2013266","journal-title":"ACM Trans Graph"},{"key":"9425_CR15","doi-asserted-by":"crossref","unstructured":"Ess A, Mueller T, Grabner H, Van Gool LJ (2009) Segmentation-based urban traffic scene understanding. In: BMVC. Citeseer, vol 1, p 2","DOI":"10.5244\/C.23.84"},{"issue":"2","key":"9425_CR16","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88 (2):303\u2013338","journal-title":"Int J Comput Vis"},{"issue":"3","key":"9425_CR17","doi-asserted-by":"publisher","first-page":"2577","DOI":"10.1016\/j.eswa.2010.08.047","volume":"38","author":"A Fern\u00e1ndez-Caballero","year":"2011","unstructured":"Fern\u00e1ndez-Caballero A, Castillo JC, Serrano-Cuerda J, Maldonado-Basc\u00f3n S (2011) Real-time human segmentation in infrared videos. Expert Syst Appl 38(3):2577\u20132584","journal-title":"Expert Syst Appl"},{"issue":"3","key":"9425_CR18","doi-asserted-by":"publisher","first-page":"033011","DOI":"10.1117\/1.2762250","volume":"16","author":"F Ge","year":"2007","unstructured":"Ge F, Wang S, Liu T (2007) New benchmark for image segmentation evaluation. J Electron Imaging 16(3):033011","journal-title":"J Electron Imaging"},{"key":"9425_CR19","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Gordon G, Dunson D, Dud\u00edk M (eds) Proceedings of the fourteenth international conference on artificial intelligence and statistics, proceedings of machine learning research, vol 15. PMLR, Fort Lauderdale, pp 315\u2013323"},{"key":"9425_CR20","volume-title":"Deep learning, vol 1","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, vol 1. MIT Press, Cambridge"},{"key":"9425_CR21","doi-asserted-by":"crossref","unstructured":"Gruosso M, Capece N, Erra U, Lopardo N (2019) A deep learning approach for the motion picture content rating. In: 2019 10th IEEE international conference on cognitive infocommunications (CogInfoCom). IEEE, pp 137\u2013142","DOI":"10.1109\/CogInfoCom47531.2019.9089897"},{"key":"9425_CR22","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1109\/34.868683","volume":"22","author":"I Haritaoglu","year":"2000","unstructured":"Haritaoglu I, Harwood D, Davis LS (2000) W4: real-time surveillance of people and their activities. IEEE Trans Pattern Anal Mach Intell 22:809\u2013830","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9425_CR23","doi-asserted-by":"crossref","unstructured":"He K, Rhemann C, Rother C, Tang X, Sun J (2011) A global sampling method for alpha matting. In: CVPR 2011, pp 2049\u20132056","DOI":"10.1109\/CVPR.2011.5995495"},{"key":"9425_CR24","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the 2015 IEEE international conference on computer vision (ICCV), ICCV \u201915. IEEE Computer Society, Washington, pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"key":"9425_CR25","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez A, Reyes M, Escalera S, Radeva P (2010) Spatio-temporal grabcut human segmentation for face and pose recovery. In: 2010 IEEE computer society conference on computer vision and pattern recognition-workshops. IEEE, pp 33\u201340","DOI":"10.1109\/CVPRW.2010.5543824"},{"key":"9425_CR26","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167"},{"key":"9425_CR27","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1125\u20131134","DOI":"10.1109\/CVPR.2017.632"},{"issue":"5","key":"9425_CR28","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1007\/s00521-017-3158-6","volume":"29","author":"F Jiang","year":"2018","unstructured":"Jiang F, Grigorev A, Rho S, Tian Z, Fu Y, Jifara W, Adil K, Liu S (2018) Medical image semantic segmentation based on deep learning. Neural Comput Appl 29(5):1257\u20131265","journal-title":"Neural Comput Appl"},{"key":"9425_CR29","doi-asserted-by":"crossref","unstructured":"Karacan L, Erdem A, Erdem E (2015) Image matting with kl-divergence based sparse sampling. In: Proceedings of the IEEE international conference on computer vision, pp 424\u2013432","DOI":"10.1109\/ICCV.2015.56"},{"key":"9425_CR30","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.isprsjprs.2018.04.014","volume":"145","author":"R Kemker","year":"2018","unstructured":"Kemker R, Salvaggio C, Kanan C (2018) Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning. ISPRS J Photogramm Remote Sens 145:60\u201377","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"9425_CR31","doi-asserted-by":"crossref","unstructured":"Kenney J, Buckley T, Brock O (2009) Interactive segmentation for manipulation in unstructured environments. In: IEEE international conference on robotics and automation, 2009. ICRA\u201909. IEEE, pp 1377\u20131382","DOI":"10.1109\/ROBOT.2009.5152393"},{"key":"9425_CR32","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980"},{"key":"9425_CR33","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2019.02.003","volume":"338","author":"F Lateef","year":"2019","unstructured":"Lateef F, Ruichek Y (2019) Survey on semantic segmentation using deep learning techniques. Neurocomputing 338:321\u2013348","journal-title":"Neurocomputing"},{"issue":"7553","key":"9425_CR34","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"9425_CR35","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision. Springer , pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"9425_CR36","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"9425_CR37","doi-asserted-by":"crossref","unstructured":"Maggiori E, Tarabalka Y, Charpiat G, Alliez P (2017) High-resolution image classification with convolutional networks. In: 2017 IEEE international geoscience and remote sensing symposium (IGARSS), pp 5157\u20135160","DOI":"10.1109\/IGARSS.2017.8128163"},{"key":"9425_CR38","doi-asserted-by":"crossref","unstructured":"Migniot C, Bertolino P, Chassery JM (2011) Automatic people segmentation with a template-driven graph cut. In: 2011 18th IEEE international conference on image processing. IEEE, pp 3149\u20133152","DOI":"10.1109\/ICIP.2011.6116335"},{"key":"9425_CR39","doi-asserted-by":"crossref","unstructured":"Morar A, Moldoveanu F, Gr\u00f6ller E (2012) Image segmentation based on active contours without edges. In: 2012 IEEE 8th international conference on intelligent computer communication and processing. IEEE, pp 213\u2013220","DOI":"10.1109\/ICCP.2012.6356188"},{"issue":"2","key":"9425_CR40","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/s11042-010-0677-x","volume":"57","author":"Y Nam","year":"2012","unstructured":"Nam Y, Rho S, Park JH (2012) Intelligent video surveillance system: 3-tier context-aware surveillance system with metadata. Multimed Tools Appl 57(2):315\u2013334","journal-title":"Multimed Tools Appl"},{"key":"9425_CR41","doi-asserted-by":"crossref","unstructured":"Noh H, Hong S, Han B (2015) Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE international conference on computer vision, pp 1520\u20131528","DOI":"10.1109\/ICCV.2015.178"},{"issue":"8","key":"9425_CR42","doi-asserted-by":"publisher","first-page":"1865","DOI":"10.1109\/TMI.2018.2806086","volume":"37","author":"AA Novikov","year":"2018","unstructured":"Novikov AA, Lenis D, Major D, Hlad\u00fcvka J, Wimmer M, B\u00fchler K (2018) Fully convolutional architectures for multiclass segmentation in chest radiographs. IEEE Trans Med Imaging 37(8): 1865\u20131876","journal-title":"IEEE Trans Med Imaging"},{"issue":"9","key":"9425_CR43","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1016\/0031-3203(93)90135-J","volume":"26","author":"NR Pal","year":"1993","unstructured":"Pal NR, Pal SK (1993) A review on image segmentation techniques. Pattern Recognit 26(9):1277\u20131294","journal-title":"Pattern Recognit"},{"key":"9425_CR44","doi-asserted-by":"crossref","unstructured":"Rhemann C, Rother C, Wang J, Gelautz M, Kohli P, Rott P (2009) A perceptually motivated online benchmark for image matting. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009. IEEE, pp 1826\u20131833","DOI":"10.1109\/CVPRW.2009.5206503"},{"key":"9425_CR45","doi-asserted-by":"crossref","unstructured":"Rosenblatt F (1961) Principles of neurodynamics. Perceptrons and the theory of brain mechanisms. Tech. rep., Cornell Aeronautical Lab Inc, Buffalo","DOI":"10.21236\/AD0256582"},{"issue":"5","key":"9425_CR46","first-page":"1","volume":"1","author":"Y Sasaki","year":"2007","unstructured":"Sasaki Y, et al. (2007) The truth of the f-measure. Teach Tutor mater 1(5):1\u20135","journal-title":"Teach Tutor mater"},{"key":"9425_CR47","doi-asserted-by":"crossref","unstructured":"Sengupta S, Jayaram V, Curless B, Seitz S, Kemelmacher-Shlizerman I (2020) Background matting: The world is your green screen. arXiv:2004.00626","DOI":"10.1109\/CVPR42600.2020.00236"},{"key":"9425_CR48","doi-asserted-by":"crossref","unstructured":"Shen X, Hertzmann A, Jia J, Paris S, Price B, Shechtman E, Sachs I (2016) Automatic portrait segmentation for image stylization. In: Proceedings of the 37th annual conference of the European association for computer graphics, EG \u201916. Eurographics Association, Goslar. DEU, pp 93\u2013102","DOI":"10.1111\/cgf.12814"},{"issue":"8","key":"9425_CR49","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1109\/34.868688","volume":"22","author":"J Shi","year":"2000","unstructured":"Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888\u2013905","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9425_CR50","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. CoRR arXiv:1409.1556"},{"key":"9425_CR51","doi-asserted-by":"crossref","unstructured":"Song C, Huang Y, Wang Z, Wang L (2015) 1000fps human segmentation with deep convolutional neural networks. In: 2015 3rd IAPR Asian conference on pattern recognition (ACPR). IEEE, pp 474\u2013478","DOI":"10.1109\/ACPR.2015.7486548"},{"issue":"1","key":"9425_CR52","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s40537-019-0212-5","volume":"6","author":"G Sreenu","year":"2019","unstructured":"Sreenu G, Durai MS (2019) Intelligent video surveillance: a review through deep learning techniques for crowd analysis. J Big Data 6(1):48","journal-title":"J Big Data"},{"issue":"1","key":"9425_CR53","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/S0034-4257(97)00083-7","volume":"62","author":"SV Stehman","year":"1997","unstructured":"Stehman SV (1997) Selecting and interpreting measures of thematic classification accuracy. Remote Sens Environ 62(1):77\u201389","journal-title":"Remote Sens Environ"},{"key":"9425_CR54","doi-asserted-by":"crossref","unstructured":"Tesema FB, Wu H, Zhu W (2018) Human segmentation with deep contour-aware network. In: Proceedings of the 2018 international conference on computing and artificial intelligence. ACM, pp 98\u2013103","DOI":"10.1145\/3194452.3194471"},{"key":"9425_CR55","doi-asserted-by":"crossref","unstructured":"Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Sixth international conference on computer vision (IEEE Cat. No.98CH36271), pp 839\u2013846","DOI":"10.1109\/ICCV.1998.710815"},{"key":"9425_CR56","doi-asserted-by":"crossref","unstructured":"Tseng YH, Jan SS (2018) Combination of computer vision detection and segmentation for autonomous driving. In: 2018 IEEE\/ION position, location and navigation symposium (PLANS). IEEE, pp 1047\u20131052","DOI":"10.1109\/PLANS.2018.8373485"},{"key":"9425_CR57","doi-asserted-by":"crossref","unstructured":"Vineet V, Warrell J, Ladicky L, Torr PH (2011) Human instance segmentation from video using detector-based conditional random fields. In: BMVC, vol 2, pp 12\u201315","DOI":"10.5244\/C.25.80"},{"key":"9425_CR58","doi-asserted-by":"crossref","unstructured":"Xu N, Price B, Cohen S, Huang T (2017) Deep image matting. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2970\u20132979","DOI":"10.1109\/CVPR.2017.41"},{"key":"9425_CR59","doi-asserted-by":"crossref","unstructured":"Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision\u2014ECCV 2014. Springer International Publishing, Cham, pp 818\u2013833","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"9425_CR60","doi-asserted-by":"crossref","unstructured":"Zeiler MD, Krishnan D, Taylor GW, Fergus R (2010) Deconvolutional networks. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 2528\u20132535","DOI":"10.1109\/CVPR.2010.5539957"},{"key":"9425_CR61","doi-asserted-by":"crossref","unstructured":"Zhang SH, Li R, Dong X, Rosin P, Cai Z, Han X, Yang D, Huang H, Hu SM (2019) Pose2seg: detection free human instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 889\u2013898","DOI":"10.1109\/CVPR.2019.00098"},{"key":"9425_CR62","doi-asserted-by":"crossref","unstructured":"Zhao T, Nevatia R (2002) Stochastic human segmentation from a static camera. In: Workshop on motion and video computing, 2002. Proceedings. IEEE, pp 9\u201314","DOI":"10.1109\/MOTION.2002.1182207"},{"key":"9425_CR63","doi-asserted-by":"crossref","unstructured":"Zhao T, Nevatia R (2003) Bayesian human segmentation in crowded situations. In: 2003 IEEE computer society conference on computer vision and pattern recognition, 2003. Proceedings, vol 2. IEEE, pp II\u2013459","DOI":"10.1109\/CVPR.2003.1211503"},{"key":"9425_CR64","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"issue":"7","key":"9425_CR65","doi-asserted-by":"publisher","first-page":"3386","DOI":"10.1109\/JSTARS.2017.2680324","volume":"10","author":"W Zhao","year":"2017","unstructured":"Zhao W, Du S, Emery WJ (2017) Object-based convolutional neural network for high-resolution imagery classification. IEEE J Sel Top Appl Earth Obs Remote Sens 10(7):3386\u20133396","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"9425_CR66","doi-asserted-by":"crossref","unstructured":"Zhou YT, Chellappa R (1988) Computation of optical flow using a neural network. In: IEEE 1988 international conference on neural networks, vol 2, pp 71\u201378","DOI":"10.1109\/ICNN.1988.23914"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09425-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-09425-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09425-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,5]],"date-time":"2021-09-05T23:14:24Z","timestamp":1630883664000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-09425-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,6]]},"references-count":66,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["9425"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09425-0","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,6]]},"assertion":[{"value":"29 August 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}