{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T03:18:28Z","timestamp":1773890308536,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T00:00:00Z","timestamp":1675987200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T00:00:00Z","timestamp":1675987200000},"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":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s12652-023-04549-1","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T19:59:51Z","timestamp":1676059191000},"page":"4371-4383","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep learning based video surveillance for predicting vehicle density in real time scenario"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6961-2994","authenticated-orcid":false,"given":"G.","family":"Priyanka","sequence":"first","affiliation":[]},{"given":"J.","family":"Senthil Kumar","sequence":"additional","affiliation":[]},{"given":"S. T.","family":"Veena","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,10]]},"reference":[{"key":"4549_CR1","doi-asserted-by":"publisher","first-page":"2161","DOI":"10.1007\/s10489-020-01995-8","volume":"51","author":"R Ahila Priyadharshini","year":"2021","unstructured":"Ahila Priyadharshini R, Arivazhagan S, Arun M (2021) A deep learning approach for person identification using ear biometrics. Appl Intell 51:2161\u20132172","journal-title":"Appl Intell"},{"key":"4549_CR4","doi-asserted-by":"publisher","first-page":"6057","DOI":"10.1007\/s12652-020-02170-0","volume":"12","author":"S Anjanadevi Bondalapati","year":"2021","unstructured":"Anjanadevi Bondalapati S, Nagakishore Bhavanam ES, Reddy (2021) Moving object detection based on unified model. J Ambient Intell Humaniz Comput 12:6057\u20136072","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"4549_CR5","doi-asserted-by":"crossref","unstructured":"Asmaa O, Mokhtar K, Abdelaziz O (2013) \u201cRoad traffic density estimation using microscopic and macroscopic parameters\u201d, Image and Vision Computing, Vol.\u00a031, Issue no. 11, pp.\u00a0887\u2013894, Nov","DOI":"10.1016\/j.imavis.2013.09.006"},{"key":"4549_CR6","doi-asserted-by":"crossref","unstructured":"Bas E, Tekalp A, Salman FS (2007) \u201cAutomatic Vehicle Counting from Video for Traffic FlowAnalysis\u201d, Proceedings of IEEE Intelligent Vehicles Symposium, pp.\u00a0392\u2013397, Jun.","DOI":"10.1109\/IVS.2007.4290146"},{"key":"4549_CR7","doi-asserted-by":"crossref","unstructured":"Chen Y, Qin R, Zhang G, Albanwan H (2021) Spatial temporal analysis of traffic patterns during the COVID-19 epidemic by vehicle detection using planet remote-sensing Satellite images.Remote Sens.13, 208","DOI":"10.3390\/rs13020208"},{"key":"4549_CR8","doi-asserted-by":"crossref","unstructured":"Dai Z, Song H, Liang H et al (2020) \u201cTraffic parameter estimation and control system based on machine vision\u201d,Journal of Ambient Intelligence and Human Computing, 1-13","DOI":"10.1007\/s12652-020-02052-5"},{"key":"4549_CR9","doi-asserted-by":"crossref","unstructured":"Derpanis KG, Wildes RP (2011) \u201cClassification of Traffic Video based on a Spatiotemporal Orientation Analysis\u201d, Proceedings of IEEE Workshop Applications of Computer Vision, pp.\u00a0606\u2013613, Jan.","DOI":"10.1109\/WACV.2011.5711560"},{"key":"4549_CR10","doi-asserted-by":"publisher","unstructured":"Dey S, Kalliatakis G, Saha S, Singh AK, Ehsan S, McDonald-Maier K, \u201cMAT-CNN-SOPC: Motionless Analysis of Traffic Using Convolutional Neural Networks on System-On-a-Programmable-Chip,\u201c 2018 NASA\/ESA Conference on Adaptive Hardware and, Systems (2018) (AHS), pp.\u00a0291\u2013298, doi: https:\/\/doi.org\/10.1109\/AHS.2018.8541406","DOI":"10.1109\/AHS.2018.8541406"},{"key":"4549_CR11","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) \u201cRich Feature Hierarchies for accurate object detection and semantic segmentation\u201d, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.\u00a0580\u2013587, Jun.","DOI":"10.1109\/CVPR.2014.81"},{"key":"4549_CR13","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2014) \u201cSpatial pyramid pooling in deep convolutional networks for visual recognition\u201d, Proceedings of 13th European Conference on Computer Vision, Springer, pp.\u00a0346\u2013361,","DOI":"10.1007\/978-3-319-10578-9_23"},{"issue":"1","key":"4549_CR14","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 Trans Pattern Anal Mach Intell 35(1):221\u2013231","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4549_CR15","doi-asserted-by":"crossref","unstructured":"Kankanamge KD, Witharanage YR, Withanage CS, Hansini M, Lakmal D, Thayasivam U (2019) \u201cTaxi Trip Travel Time Prediction with Isolated XGBoost Regression,\u201c 2019 Moratuwa Engineering Research Conference (MERCon), pp.\u00a054\u201359","DOI":"10.1109\/MERCon.2019.8818915"},{"key":"4549_CR16","unstructured":"Karen Simonyan, Zisserman A (2014) \u201cTwo-Stream Convolutional Networks for Action Recognition in Videos\u201d, Proceedings of Advanced Neural Inference Processing System, pp.\u00a0568\u2013576, Nov"},{"key":"4549_CR17","doi-asserted-by":"crossref","unstructured":"Kaviani R, Ahmadi P, Gholampour I (2015) \u201cA new method for traffic density estimation based on topic model\u201d, 2015 Signal Processing and Intelligent Systems Conference (SPIS), pp.\u00a0114\u2013118,","DOI":"10.1109\/SPIS.2015.7422323"},{"key":"4549_CR18","doi-asserted-by":"crossref","unstructured":"Kilic E, Ozturk S (2021) \u201cAn accurate car counting in aerial images based on convolutional neural networks\u201d,Journal of Ambient Intelligence and Humanized Computing,","DOI":"10.1007\/s12652-021-03377-5"},{"key":"4549_CR19","unstructured":"Krizhevsky I, Sutskever, Hinton GE (2012) \u201cImageNet Classification with Deep Convolutional Neural Networks\u201d, Proceedings of Advanced Neural Inference Processing System (NIPS), pp.\u00a01097\u20131105,"},{"key":"4549_CR20","doi-asserted-by":"publisher","first-page":"2453","DOI":"10.1016\/j.sbspro.2013.08.274","volume":"96","author":"X Li","year":"2013","unstructured":"Li X, She Y, Luo D, Yu Z (2013) A Traffic State Detection Tool for Freeway Video Surveillance System. Procedia \u2013 Social Behavioral Sciences 96:2453\u20132461","journal-title":"Procedia \u2013 Social Behavioral Sciences"},{"key":"4549_CR21","unstructured":"Liang Hu, Wang L, Zhou Z, Sheng Z, Zhang Y (2021) \u201cNetwork-wide Traffic Signal Optimization under Connected Vehicles Environment\u201d, IEEE International Intelligent Transportation Systems Conference (ITSC),"},{"key":"4549_CR2","doi-asserted-by":"crossref","unstructured":"Louati A, Louati H, Nusir M (2020a) Multi-agent deep neural networks coupled with LQF\u2010MWM algorithm for traffic control and emergency vehicles guidance\u201d. J Ambient Intell Humaniz Comput 11:5611\u20135627","DOI":"10.1007\/s12652-020-01921-3"},{"key":"4549_CR22","doi-asserted-by":"publisher","first-page":"5611","DOI":"10.1007\/s12652-020-01921-3","volume":"11","author":"A Louati","year":"2020","unstructured":"Louati A, Louati H, Nusir M et al (2020b) Multi-agent deep neural networks coupled with LQF-MWM algorithm for traffic control and emergency vehicles guidance. J Ambient Intell Hum Comput 11:5611\u20135627","journal-title":"J Ambient Intell Hum Comput"},{"key":"4549_CR23","doi-asserted-by":"crossref","unstructured":"Luo Z, Jodoin PM, Li SZ, Su SZ (2015) \u201cTraffic analysis without motion features\u201d, Proceedings of IEEE Conference on Image Processing,Vol\u00a02, pp.\u00a03290\u20133294, Sep.","DOI":"10.1109\/ICIP.2015.7351412"},{"issue":"4","key":"4549_CR24","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1109\/TCSVT.2016.2632439","volume":"28","author":"Z Luo","year":"2018","unstructured":"Luo Z, Jodoin PM, Su SZ, Li SZ, Larochelle H (2018a) Traffic analytics with low-frame-rate videos. IEEE Trans Circuits Syst Video Technol 28(4):878\u2013891","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"4549_CR25","unstructured":"Luong Anh Tuan Nguyen and Thanh Xuan Ha (2021) A Novel Approach of Traffic Density Estimation Using CNNs and Computer Vision. EJECE, European Journal of Electrical Engineering and Computer Science Vol(4)"},{"key":"4549_CR42","doi-asserted-by":"crossref","unstructured":"Luo Z, Jodoin P-M, Su S-Z, Li S-Z, Larochelle H (2018b) \u201cTraffic Analytics With Low-Frame-Rate Videos\u201d,IEEE Transactions On Circuits And Systems For Video Technology, Vol.\u00a028, No. 4, April","DOI":"10.1109\/TCSVT.2016.2632439"},{"issue":"7","key":"4549_CR26","doi-asserted-by":"publisher","first-page":"7375","DOI":"10.1007\/s12652-020-02413-0","volume":"12","author":"SD Mahalakshmi","year":"2021","unstructured":"Mahalakshmi SD, Vijayalakshmi K (2021) Agro Suraksha: pest and disease detection for corn field using image analysis. J Ambient Intell Humaniz Comput 12(7):7375\u20137389","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"4549_CR28","doi-asserted-by":"crossref","unstructured":"Miller N, Thomas MA, Eichel JA, Mishra A (2015) \u201cA hidden Markov model for vehicle detection and counting\u201d, Proceedings of 12th IEEE Conference on Computer and Robot Vision (CRV), pp.\u00a0269\u2013276, June","DOI":"10.1109\/CRV.2015.42"},{"issue":"1","key":"4549_CR29","first-page":"91","volume":"3","author":"A Mohamed","year":"2022","unstructured":"Mohamed A, Abdelwahab (2022) Robust traffic congestion recognition in videos based on deep Multi-Stream LSTM. SVU-International J Eng Sci Appl 3(1):91\u201397","journal-title":"SVU-International J Eng Sci Appl"},{"key":"4549_CR30","doi-asserted-by":"crossref","unstructured":"Priyanka G, Pavithra S (2019) \u201cFacial expression recognition using SVM with CNN and Handcrafted features. International Journal of Recent Technology and Engineering (IJRTE)","DOI":"10.35940\/ijrte.D7802.118419"},{"key":"4549_CR31","unstructured":"Priyanka, G., T. Revathi, K. Muneeswaran (2019) \u201cAutomatic caption generation from images based on facial Emotions\u201d. International Journal of Recent Technology and Engineering (IJRTE)"},{"key":"4549_CR32","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.dsp.2019.03.017","volume":"92","author":"LC Ribas","year":"2019","unstructured":"Ribas LC, Goncalves WN, Bruno OM (2019) Dynamic texture analysis with diffusion in networks. Digit Signal Proc 92:109\u2013126","journal-title":"Digit Signal Proc"},{"key":"4549_CR33","doi-asserted-by":"crossref","unstructured":"Russel NS, Selvaraj A (2021) Fusion of spatial and dynamic CNN streams for action recognition.Multimedia Systems,1\u201316","DOI":"10.1007\/s00530-021-00773-x"},{"issue":"2","key":"4549_CR34","first-page":"106","volume":"13","author":"D Selvathi","year":"2021","unstructured":"Selvathi D, Suganya K, Menaka M, Venkatraman B (2021) Deep convolutional neural network-based diabetic eye disease detection and classification using thermal images. Int J Reasoning-based Intell Syst 13(2):106\u2013114","journal-title":"Int J Reasoning-based Intell Syst"},{"issue":"3","key":"4549_CR35","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1049\/iet-ipr.2019.0271","volume":"14","author":"H Sikkandar","year":"2020","unstructured":"Sikkandar H, Thiyagarajan R (2020) Soft biometrics-based face image retrieval using improved grey wolf optimisation. IET Image Proc 14(3):451\u2013461","journal-title":"IET Image Proc"},{"key":"4549_CR36","doi-asserted-by":"crossref","unstructured":"Sobral L, Oliveira L, Schnitman, Souza F (2013) \u201cHighway Traffic Congestion Classification using Holistic Properties\u201d, Proceedings of 10th IASTED International Conference of Signal Processing and PatternRecognition Applications, pp.\u00a0458\u2013465,","DOI":"10.2316\/P.2013.798-105"},{"key":"4549_CR37","doi-asserted-by":"publisher","first-page":"34311","DOI":"10.1007\/s11042-021-10931-y","volume":"80","author":"CS Sung","year":"2021","unstructured":"Sung CS, Park JY (2021) Correction to: design of an intelligent video surveillance system for crime prevention: applying deep learning technology. Multimedia Tools Applications 80:34311","journal-title":"Multimedia Tools Applications"},{"key":"4549_CR38","doi-asserted-by":"crossref","unstructured":"Wang Y, Wang L, Kong D, Yin B (2018) \u201cExtrinsic least squares regression with closed-form solution on product grassmann manifold for video-based recognition,\u201d Mathematical Problems in Engineering, vol. no. 1, pp.\u00a01\u20137, 2018","DOI":"10.1155\/2018\/6598025"},{"key":"4549_CR39","doi-asserted-by":"crossref","unstructured":"Yiren Zhou, HosseinNejati T-T, Do N-M, Cheung L, Cheah (2016) \u201cImage-based Vehicle Analysis using Deep Neural Network: A Systematic Study\u201d, IEEE Conference on Computer Vision and Pattern Recognition, August","DOI":"10.1109\/ICDSP.2016.7868561"},{"key":"4549_CR27","doi-asserted-by":"publisher","first-page":"8507","DOI":"10.1007\/s12652-020-02585-9","volume":"12","author":"MB Younes","year":"2021","unstructured":"Younes MB (2021) Real-time traffic distribution prediction protocol (TDPP) for vehicular networks. J Ambient Intell Humaniz Comput 12:8507\u20138518","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"4549_CR40","doi-asserted-by":"crossref","unstructured":"Zhang W, Chen L, Gong W, Li Z, Lu Q, Yang S (2015) \u201cAn integrated approach for vehicle detection and type recognition\u201d, Proceedings of the IEEE 12th International Conference on UIC-ATC-ScalCom, pp.\u00a0798\u2013801, Aug.","DOI":"10.1109\/UIC-ATC-ScalCom-CBDCom-IoP.2015.157"},{"issue":"4","key":"4549_CR41","doi-asserted-by":"publisher","first-page":"2247","DOI":"10.1109\/TITS.2015.2402438","volume":"16","author":"Y Zhen Dong","year":"2015","unstructured":"Zhen Dong Y, Wu M, Pei YundeJia (2015) Vehicle type classification using a semisupervised convolutional neural network. IEEE Trans Intell Transp Syst 16(4):2247\u20132256","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"4549_CR12","doi-asserted-by":"crossref","unstructured":"Zhang H, Xiao Z, Wang J, Li F, Szczerbicki E (2019) \u201cA Novel IoT-Perceptive Human Activity Recognition (HAR) Approach using Multi-Head Convolutional Attention\u201d,IEEE Internet of Things Journal,","DOI":"10.1109\/JIOT.2019.2949715"},{"key":"4549_CR43","doi-asserted-by":"crossref","unstructured":"Zhiwen Xiao X, Xu H, Zhang E, Szczerbicki (2021) \u201cA new multi-process collaborative architecture for time series classification\u201d,Knowledge-Based Systems,","DOI":"10.1016\/j.knosys.2021.106934"},{"key":"4549_CR44","doi-asserted-by":"crossref","unstructured":"Zhu C, Li B, Wang K, Yuan, Yang Z (2019) \u201cDCGSA: A global selfattention network with dilated convolution for crowd density map generating\u201d, Neurocomputing,","DOI":"10.1016\/j.neucom.2019.10.081"},{"key":"4549_CR45","doi-asserted-by":"crossref","unstructured":"Zou ZK, Cheng Y, Qu XY, Ji SL, Guo XX, Zhou P (2019) \u201cAttend to count: Crowd counting with adaptive capacity multi-scale CNNs\u201d, Neurocomputing, Article vol.\u00a0367, pp.\u00a075\u201383, Nov","DOI":"10.1016\/j.neucom.2019.08.009"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-023-04549-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-023-04549-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-023-04549-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T12:07:55Z","timestamp":1680005275000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-023-04549-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,10]]},"references-count":44,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["4549"],"URL":"https:\/\/doi.org\/10.1007\/s12652-023-04549-1","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,10]]},"assertion":[{"value":"29 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}