{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:26:09Z","timestamp":1740122769265,"version":"3.37.3"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100012725","name":"Tecnol\u00f3gico Nacional de M\u00e9xico","doi-asserted-by":"publisher","award":["5162.19-P","7598.20-P"],"award-info":[{"award-number":["5162.19-P","7598.20-P"]}],"id":[{"id":"10.13039\/100012725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s11063-022-11092-1","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T07:26:03Z","timestamp":1669793163000},"page":"5425-5449","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Moving Object Detection in Video Sequences Based on a Two-Frame Temporal Information CNN"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5382-9424","authenticated-orcid":false,"given":"Mario I.","family":"Chacon-Murguia","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0819-0438","authenticated-orcid":false,"given":"Abimael","family":"Guzman-Pando","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,30]]},"reference":[{"key":"11092_CR1","doi-asserted-by":"crossref","unstructured":"Babiker M, Khalifa OO, Htike KK, et al (2017) Automated daily human activity recognition for video surveillance using neural network. In: 2017 IEEE international conference on smart instrumentation, measurement and applications, ICSIMA 2017. pp 1\u20135","DOI":"10.1109\/ICSIMA.2017.8312024"},{"key":"11092_CR2","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s00521-018-3692-x","volume":"31","author":"S Xie","year":"2019","unstructured":"Xie S, Zhang X, Cai J (2019) Video crowd detection and abnormal behavior model detection based on machine learning method. Neural Comput Appl 31:175\u2013184. https:\/\/doi.org\/10.1007\/s00521-018-3692-x","journal-title":"Neural Comput Appl"},{"key":"11092_CR3","doi-asserted-by":"publisher","first-page":"2501","DOI":"10.1007\/s11063-020-10325-5","volume":"53","author":"D Li","year":"2021","unstructured":"Li D, Qin B, Liu W, Deng L (2021) A city monitoring system based on real-time communication interaction module and intelligent visual information collection system. Neural Process Lett 53:2501\u20132517. https:\/\/doi.org\/10.1007\/s11063-020-10325-5","journal-title":"Neural Process Lett"},{"key":"11092_CR4","first-page":"113","volume":"517","author":"B Zhang","year":"2020","unstructured":"Zhang B, Guo K, Yang Y et al (2020) Pedestrian detection based on deep neural network in video surveillance. Commun Signal Process Syst Lect Notes Electr Eng 517:113\u2013120","journal-title":"Commun Signal Process Syst Lect Notes Electr Eng"},{"key":"11092_CR5","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.neucom.2018.01.092","volume":"300","author":"A Brunetti","year":"2018","unstructured":"Brunetti A, Buongiorno D, Trotta GF, Bevilacqua V (2018) Computer vision and deep learning techniques for pedestrian detection and tracking: A survey. Neurocomputing 300:17\u201333. https:\/\/doi.org\/10.1016\/j.neucom.2018.01.092","journal-title":"Neurocomputing"},{"key":"11092_CR6","doi-asserted-by":"publisher","unstructured":"Serrano MM, Chen YP, Howard A, Vela PA (2016) Automated feet detection for clinical gait assessment. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS) 2016-October. pp 2161\u20132164. https:\/\/doi.org\/10.1109\/EMBC.2016.7591157","DOI":"10.1109\/EMBC.2016.7591157"},{"key":"11092_CR7","doi-asserted-by":"crossref","unstructured":"Moro M, Marchesi G, Odone F, Casadio M (2020) Markerless gait analysis in stroke survivors based on computer vision and deep learning. In: Proceedings of the 35th Annual ACM Symposium on Applied Computing. pp 2097\u20132104","DOI":"10.1145\/3341105.3373963"},{"key":"11092_CR8","first-page":"1","volume":"11059","author":"B Magnier","year":"2019","unstructured":"Magnier B, Gabbay E, Bougamale F et al (2019) Multiple honey bees tracking and trajectory modeling. Multimodal Sens Artif Intell Technol Appl Int Soc Opt Photonics 11059:1\u201312","journal-title":"Multimodal Sens Artif Intell Technol Appl Int Soc Opt Photonics"},{"key":"11092_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2139\/ssrn.3564057","volume":"3564057","author":"D Shakeel","year":"2020","unstructured":"Shakeel D, Bakshi G, Singh B (2020) insect detection and flight tracking in a controlled environment using machine vision: review of existing techniques and an improved approach. SSRN Electron J 3564057:1\u20136. https:\/\/doi.org\/10.2139\/ssrn.3564057","journal-title":"SSRN Electron J"},{"key":"11092_CR10","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.cosrev.2014.04.001","volume":"11\u201312","author":"T Bouwmans","year":"2014","unstructured":"Bouwmans T (2014) Traditional and recent approaches in background modeling for foreground detection: an overview. Comput Sci Rev 11\u201312:31\u201366. https:\/\/doi.org\/10.1016\/j.cosrev.2014.04.001","journal-title":"Comput Sci Rev"},{"key":"11092_CR11","doi-asserted-by":"publisher","first-page":"023025","DOI":"10.1117\/1.JEI.26.2.023025","volume":"26","author":"K Sehairi","year":"2017","unstructured":"Sehairi K, Chouireb F, Meunier J (2017) Comparative study of motion detection methods for video surveillance systems. J Electron Imaging 26:023025. https:\/\/doi.org\/10.1117\/1.JEI.26.2.023025","journal-title":"J Electron Imaging"},{"key":"11092_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cosrev.2019.100204","volume":"35","author":"B Garcia-Garcia","year":"2020","unstructured":"Garcia-Garcia B, Bouwmans T, Rosales Silva AJ (2020) Background subtraction in real applications: challenges, current models and future directions. Comput Sci Rev 35:1\u201342. https:\/\/doi.org\/10.1016\/j.cosrev.2019.100204","journal-title":"Comput Sci Rev"},{"key":"11092_CR13","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.neucom.2022.06.104","volume":"503","author":"X Zhao","year":"2022","unstructured":"Zhao X, Wang G, He Z, Jiang H (2022) A survey of moving object detection methods: a practical perspective. Neurocomputing 503:28\u201348. https:\/\/doi.org\/10.1016\/j.neucom.2022.06.104","journal-title":"Neurocomputing"},{"key":"11092_CR14","doi-asserted-by":"publisher","unstructured":"Kulchandani JS, Dangarwala KJ (2015) Moving object detection: review of recent research trends. Pervasive Comput (ICPC), 2015 Int Conf 1:1\u20135. https:\/\/doi.org\/10.1109\/PERVASIVE.2015.7087138","DOI":"10.1109\/PERVASIVE.2015.7087138"},{"key":"11092_CR15","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1109\/TLA.2019.8986414","volume":"17","author":"A Guzman-Pando","year":"2019","unstructured":"Guzman-Pando A, Chacon-Murguia MI (2019) Analysis and trends on moving object detection algorithm techniques. IEEE Lat Am Trans 17:1771\u20131783","journal-title":"IEEE Lat Am Trans"},{"key":"11092_CR16","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.neucom.2018.03.001","volume":"314","author":"M Wu","year":"2018","unstructured":"Wu M, Sun Y, Hang R et al (2018) Multi-component group sparse RPCA model for motion object detection under complex dynamic background. Neurocomputing 314:12\u2013131","journal-title":"Neurocomputing"},{"key":"11092_CR17","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.neucom.2018.05.097","volume":"312","author":"P Szymczyk","year":"2018","unstructured":"Szymczyk P, Szymczyk M (2018) Identification of dynamic object using Z-transform artificial neural network. Neurocomputing 312:382\u2013389","journal-title":"Neurocomputing"},{"key":"11092_CR18","doi-asserted-by":"crossref","unstructured":"Braham M, Van Droogenbroeck M (2016) Deep background subtraction with scene-specific convolutional neural networks. In: International conference on systems, signals, and image processing. pp 3\u20136","DOI":"10.1109\/IWSSIP.2016.7502717"},{"key":"11092_CR19","doi-asserted-by":"crossref","unstructured":"Babaee M, Dinh DT, Rigoll G (2017) A deep convolutional neural network for background subtraction. Comput Res Repos arXiv:1702.01731","DOI":"10.1016\/j.patcog.2017.09.040"},{"key":"11092_CR20","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.patrec.2016.09.014","volume":"96","author":"Y Wang","year":"2016","unstructured":"Wang Y, Luo Z, Jodoin PM (2016) Interactive deep learning method for segmenting moving objects. Pattern Recognit Lett 96:66\u201375. https:\/\/doi.org\/10.1016\/j.patrec.2016.09.014","journal-title":"Pattern Recognit Lett"},{"key":"11092_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIP.2015.2427519","volume":"7149","author":"Z Zhao","year":"2015","unstructured":"Zhao Z, Zhang X, Fang Y, Member S (2015) Stacked multi-layer self-organizing map for background modeling. IEEE Trans IMAGE Process 7149:1\u201310. https:\/\/doi.org\/10.1109\/TIP.2015.2427519","journal-title":"IEEE Trans IMAGE Process"},{"key":"11092_CR22","doi-asserted-by":"crossref","unstructured":"Heo B, Yun K, Choi JY (2017) Appearance and motion based deep learning architecture for moving object detection in moving camera. In: 2017 IEEE international conference on image processing (ICIP). pp 1827\u20131831","DOI":"10.1109\/ICIP.2017.8296597"},{"key":"11092_CR23","unstructured":"Rahmon G, Bunyak F, Seetharaman G, Palaniappan K (2020) Motion U-net: Multi-cue encoder-decoder network for motion segmentation. In: Proceedings - International Conference on Pattern Recognition. pp 8125\u20138132"},{"key":"11092_CR24","doi-asserted-by":"crossref","unstructured":"Lim LA, Keles HY (2018) Foreground segmentation using a triplet convolutional neural network for multiscale feature encoding. arXiv Pre-print arXiv:1801.02225","DOI":"10.1016\/j.patrec.2018.08.002"},{"key":"11092_CR25","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.patrec.2018.08.002","volume":"112","author":"LA Lim","year":"2018","unstructured":"Lim LA, Yalim Keles H (2018) Foreground segmentation using convolutional neural networks for multiscale feature encoding. Pattern Recognit Lett 112:256\u2013262. https:\/\/doi.org\/10.1016\/j.patrec.2018.08.002","journal-title":"Pattern Recognit Lett"},{"key":"11092_CR26","doi-asserted-by":"publisher","first-page":"53849","DOI":"10.1109\/ACCESS.2021.3071163","volume":"9","author":"MO Tezcan","year":"2021","unstructured":"Tezcan MO, Ishwar P, Konrad J (2021) BSUV-Net 2.0: spatio-temporal data augmentations for video-agnostic supervised background subtraction. IEEE Access 9:53849\u201353860. https:\/\/doi.org\/10.1109\/ACCESS.2021.3071163","journal-title":"IEEE Access"},{"key":"11092_CR27","doi-asserted-by":"crossref","unstructured":"Dosovitskiy A, Fischery P, Ilg E, et al (2015) FlowNet: learning optical flow with convolutional networks. In: IEEE International Conference on Computer Vision (ICCV). pp 2758\u20132766","DOI":"10.1109\/ICCV.2015.316"},{"key":"11092_CR28","first-page":"1","volume":"2014","author":"D Eigen","year":"2014","unstructured":"Eigen D, Puhrsch C, Fergus R (2014) Depth map prediction from a single image using a multi-scale deep network. Neural Inf Process Syst Conf 2014:1\u20139","journal-title":"Neural Inf Process Syst Conf"},{"key":"11092_CR29","doi-asserted-by":"crossref","unstructured":"Shafiee MJ, Siva P, Fieguth P, Wong A (2016) Embedded motion detection via neural response mixture background modeling. In: 2016 IEEE conference on computer vision and pattern recognition workshops (CVPRW). pp 19\u201326","DOI":"10.1109\/CVPRW.2016.109"},{"key":"11092_CR30","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278\u20132323. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc IEEE"},{"key":"11092_CR31","doi-asserted-by":"publisher","first-page":"1369","DOI":"10.1007\/s10044-019-00845-9","volume":"23","author":"LA Lim","year":"2020","unstructured":"Lim LA, Keles HY (2020) Learning multi-scale features for foreground segmentation. Pattern Anal Appl 23:1369\u20131380","journal-title":"Pattern Anal Appl"},{"key":"11092_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TCSVT.2021.3055539","volume":"8215","author":"F Gao","year":"2021","unstructured":"Gao F, Li Y, Lu S (2021) Extracting moving objects more accurately: a CDA contour optimizer. IEEE Trans Circuits Syst Video Technol 8215:1\u201310. https:\/\/doi.org\/10.1109\/TCSVT.2021.3055539","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"11092_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TITS.2017.2754099","volume":"19","author":"L Yang","year":"2017","unstructured":"Yang L, Li J, Member S et al (2017) Deep background modeling using fully convolutional network. IEEE Trans Intell Transp Syst 19:1\u20139. https:\/\/doi.org\/10.1109\/TITS.2017.2754099","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"11092_CR34","doi-asserted-by":"publisher","first-page":"23023","DOI":"10.1007\/s11042-017-5460-9","volume":"77","author":"D Sakkos","year":"2017","unstructured":"Sakkos D, Liu H, Han J et al (2017) End-to-end video background subtraction with 3d convolutional neural networks. Multimed Tools Appl 77:23023\u201323041. https:\/\/doi.org\/10.1007\/s11042-017-5460-9","journal-title":"Multimed Tools Appl"},{"key":"11092_CR35","doi-asserted-by":"crossref","unstructured":"Ilg E, Mayer N, Saikia T, et al (2016) FlowNet 2.0: evolution of optical flow estimation with deep networks. In: IEEE international conference on computer vision (ICCV). pp 1\u20139","DOI":"10.1109\/CVPR.2017.179"},{"key":"11092_CR36","doi-asserted-by":"publisher","first-page":"1847","DOI":"10.1016\/j.enbuild.2012.02.004","volume":"35","author":"N Kruger","year":"2013","unstructured":"Kruger N, Janssen P, Kalkan S et al (2013) Deep hierarchies in the primate visual cortex: What can we learn for computer vision? IEEE Trans Pattern Anal Mach Intell 35:1847\u20131870. https:\/\/doi.org\/10.1016\/j.enbuild.2012.02.004","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11092_CR37","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1049\/iet-cvi.2019.0997","volume":"14","author":"A Guzman-Pando","year":"2020","unstructured":"Guzman-Pando A, Chacon-Murguia MI, Chacon-Diaz LB (2020) Human-like evaluation method for object motion detection algorithms. IET Comput Vis 14:674\u2013682. https:\/\/doi.org\/10.1049\/iet-cvi.2019.0997","journal-title":"IET Comput Vis"},{"key":"11092_CR38","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.imavis.2019.04.006","volume":"88","author":"MI Chacon-Murguia","year":"2019","unstructured":"Chacon-Murguia MI, Guzman-Pando A, Ramirez-Alonso G, Ramirez-Quintana JA (2019) A novel instrument to compare dynamic object detection algorithms. Image Vis Comput 88:19\u201328. https:\/\/doi.org\/10.1016\/j.imavis.2019.04.006","journal-title":"Image Vis Comput"},{"key":"11092_CR39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539957","author":"MD Zeiler","year":"2010","unstructured":"Zeiler MD, Krishnan D, Taylor GW, Fergus R (2010) Deconvolutional networks. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. https:\/\/doi.org\/10.1109\/CVPR.2010.5539957","journal-title":"Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit"},{"key":"11092_CR40","doi-asserted-by":"crossref","unstructured":"Zeiler MD, Taylor GW, Fergus R (2011) Adaptive deconvolutional networks for mid and high level feature learning. In: In ICCV. pp 2018\u20132025","DOI":"10.1109\/ICCV.2011.6126474"},{"key":"11092_CR41","first-page":"818","volume":"2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. ECCV 2014:818\u2013833","journal-title":"ECCV"},{"key":"11092_CR42","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings - 30th ieee conference on computer vision and pattern recognition, CVPR 2017. pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"11092_CR43","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image Recognition. In: Computer vision and pattern recognition (CVPR). pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11092_CR44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965","author":"J Long","year":"2015","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. https:\/\/doi.org\/10.1109\/CVPR.2015.7298965","journal-title":"Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit"},{"key":"11092_CR45","doi-asserted-by":"publisher","first-page":"4806","DOI":"10.1109\/ACCESS.2019.2962617","volume":"8","author":"Y Ho","year":"2020","unstructured":"Ho Y, Wookey S (2020) The real-world-weight cross-entropy loss function: modeling the costs of mislabeling. IEEE Access 8:4806\u20134813. https:\/\/doi.org\/10.1109\/ACCESS.2019.2962617","journal-title":"IEEE Access"},{"key":"11092_CR46","unstructured":"Perez L, Wang J (2017) The effectiveness of data augmentation in image classification using deep learning. arXiv:1712.04621 1:1\u20138"},{"key":"11092_CR47","doi-asserted-by":"crossref","unstructured":"Taylor L, Nitschke G (2018) Improving deep learning with generic data augmentation. In: 2018 IEEE symposium series on computational intelligence (SSCI). pp 1542\u20131547","DOI":"10.1109\/SSCI.2018.8628742"},{"key":"11092_CR48","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data. https:\/\/doi.org\/10.1186\/s40537-019-0197-0","journal-title":"J Big Data"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11092-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-11092-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11092-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T16:10:51Z","timestamp":1696003851000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-11092-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,30]]},"references-count":48,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["11092"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-11092-1","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2022,11,30]]},"assertion":[{"value":"15 November 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}