{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T15:58:24Z","timestamp":1783007904154,"version":"3.54.5"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2021,1,27]],"date-time":"2021-01-27T00:00:00Z","timestamp":1611705600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,27]],"date-time":"2021-01-27T00:00:00Z","timestamp":1611705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s00500-021-05576-w","type":"journal-article","created":{"date-parts":[[2021,1,27]],"date-time":"2021-01-27T11:03:18Z","timestamp":1611745398000},"page":"11929-11940","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["Real-time image enhancement for an automatic automobile accident detection through CCTV using deep learning"],"prefix":"10.1007","volume":"25","author":[{"given":"Manu S.","family":"Pillai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gopal","family":"Chaudhary","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manju","family":"Khari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5541-6319","authenticated-orcid":false,"given":"Rub\u00e9n Gonz\u00e1lez","family":"Crespo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,27]]},"reference":[{"key":"5576_CR1","doi-asserted-by":"crossref","unstructured":"Ahangari S, Jeihani M, Dehzangi A (2019) A machine learning distracted driving prediction model. In: International symposium of intelligent unmanned systems on artificial intelligence.","DOI":"10.1145\/3387168.3387198"},{"key":"5576_CR2","unstructured":"Alwan ZS, Muhammed H, Alshaibani A (2016) Car accident detection and notification system using smartphone. Int. J. Comput. Sci. Mob. Comput.(January)."},{"key":"5576_CR3","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.aap.2019.01.014","volume":"127","author":"R Arvin","year":"2019","unstructured":"Arvin R, Kamrani M, Khattak AJ (2019) How instantaneous driving behavior contributes to crashes at intersections: extracting useful information from connected vehicle message data. Accid Anal Prev 127:118\u2013133","journal-title":"Accid Anal Prev"},{"key":"5576_CR4","unstructured":"Azimi G, Asgari H, Rahimi A, Xia J (2019) Investigation of heterogeneity in severity analysis for large truck crashes. In: 98th Annu. Meet. Transp. Res. Board."},{"key":"5576_CR5","doi-asserted-by":"crossref","unstructured":"Behrendt K (2019) Boxy vehicle detection in large images. In: Proceedings of the IEEE international conference on computer vision workshops (pp. 0\u20130).","DOI":"10.1109\/ICCVW.2019.00112"},{"key":"5576_CR6","doi-asserted-by":"crossref","unstructured":"Bewley A, Ge Z, Ott L, Ramos F, Upcroft B (2016) Simple online and realtime tracking. In: 2016 IEEE international conference on image processing (ICIP) (pp. 3464\u20133468). IEEE.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"5576_CR7","doi-asserted-by":"crossref","unstructured":"Bucilu\u01ce C, Caruana R, Niculescu-Mizil A (2006) Model compression. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 535\u2013541).","DOI":"10.1145\/1150402.1150464"},{"key":"5576_CR8","doi-asserted-by":"publisher","unstructured":"Chen Y, Yu Y, Li T (2016b) A vision based traffic accident detection method using extreme learning machine. In: Int. Conf. Adv. Robot. Mechatronics 567\u2013572. https:\/\/doi.org\/10.1109\/ICARM.2016.7606983.","DOI":"10.1109\/ICARM.2016.7606983"},{"key":"5576_CR9","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.aap.2015.05.018","volume":"82","author":"N Dong","year":"2015","unstructured":"Dong N, Huang H, Zheng L (2015) Support vector machine in crash prediction at the level of traffic analysis zones: assessing the spatial proximity effects. Accid Anal Prev 82:192\u2013198. https:\/\/doi.org\/10.1016\/j.aap.2015.05.018","journal-title":"Accid Anal Prev"},{"key":"5576_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.vehcom.2015.11.001","volume":"3","author":"B Fernandes","year":"2016","unstructured":"Fernandes B, Alam M, Gomes V, Ferreira J, Oliveira A (2016) Automatic accident detection with multi-modal alert system implementation for ITS. Veh Commun 3:1\u201311. https:\/\/doi.org\/10.1016\/j.vehcom.2015.11.001","journal-title":"Veh Commun"},{"key":"5576_CR11","volume-title":"2015","author":"Global status report on road safety","year":"2015","unstructured":"Global status report on road safety (2015) 2015. World Heal, Organ"},{"key":"5576_CR12","doi-asserted-by":"publisher","unstructured":"Golshani N, Shabanpour R, Mahmoudifard SM, Derrible S, Mohammadian A (2018) Modeling travel mode and timing decisions: comparison of artificial neural networks and copula-based joint model. Travel Behav. Soc. (September 2017), 21\u201332. https:\/\/doi.org\/10.1016\/j.tbs.2017.09.003.","DOI":"10.1016\/j.tbs.2017.09.003"},{"key":"5576_CR13","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.trc.2016.02.011","volume":"67","author":"Y Gu","year":"2016","unstructured":"Gu Y, Sean Z, Chen F (2016) From Twitter to detector: real-time traffic incident detection using social media data. Transp Res Part C 67:321\u2013342. https:\/\/doi.org\/10.1016\/j.trc.2016.02.011","journal-title":"Transp Res Part C"},{"key":"5576_CR14","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770\u2013778).","DOI":"10.1109\/CVPR.2016.90"},{"key":"5576_CR15","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531."},{"issue":"7","key":"5576_CR16","first-page":"70","volume":"5","author":"KHM Kumar","year":"2019","unstructured":"Kumar KHM, Harish BS (2019) Automatic irony detection using feature fusion and ensemble classifier. Int J Interact Multimed Artif Intell 5(7):70\u201379","journal-title":"Int J Interact Multimed Artif Intell"},{"key":"5576_CR17","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 (pp. 740\u2013755). Springer, Cham.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"5576_CR18","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In: European conference on computer vision (pp. 21\u201337). Springer, Cham.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"5576_CR19","doi-asserted-by":"publisher","unstructured":"Maaloul B, Taleb-ahmed A, Niar S, Harb N, Valderrama C (2017) Adaptive videobased algorithm for accident detection on highways. In: 12th IEEE Int. Symp. Ind. Embed. Syst. 1\u20136. https:\/\/doi.org\/10.1109\/SIES.2017.7993382.","DOI":"10.1109\/SIES.2017.7993382"},{"key":"5576_CR20","unstructured":"Traffic Incident Management (2013) Fed. Highw. Adm. Traffic safety facts 2012: Young drivers,\n2014. Natl. Highw. Traffic Saf. Adm"},{"key":"5576_CR21","first-page":"140","volume":"170","author":"A Mansourkhaki","year":"2016","unstructured":"Mansourkhaki A, Karimpour A, Sadoghi Yazdi H (2016) Non-stationary concept of accident prediction. Proc Inst Civil Eng Transp 170:140\u2013151","journal-title":"Proc Inst Civil Eng Transp"},{"issue":"5","key":"5576_CR22","doi-asserted-by":"publisher","first-page":"1912","DOI":"10.1007\/s12205-016-0495-4","volume":"21","author":"A Mansourkhaki","year":"2017","unstructured":"Mansourkhaki A, Karimpour A, Sadoghi Yazdi H (2017) Introducing prior knowledge for a hybrid accident prediction model. KSCE J Civil Eng 21(5):1912\u20131918","journal-title":"KSCE J Civil Eng"},{"key":"5576_CR23","doi-asserted-by":"publisher","DOI":"10.1177\/0361198119845899","volume-title":"Improved support vector machine models for work zone crash injury severity prediction and analysis","author":"S Mokhtarimousavi","year":"2019","unstructured":"Mokhtarimousavi S, Anderson JC, Azizinamini A, Hadi M (2019) Improved support vector machine models for work zone crash injury severity prediction and analysis. Res. Rec, Transp. https:\/\/doi.org\/10.1177\/0361198119845899"},{"key":"5576_CR24","doi-asserted-by":"publisher","DOI":"10.1080\/19439962.2019.1701166","author":"H Nasr Esfahani","year":"2019","unstructured":"Nasr Esfahani H, Arvin R, Song Z, Sze NN (2019) Prevalence of cell phone use while driving and its impact on driving performance, focusing on near-crash risk: a survey study in Tehran. J Transp Safety Secur. https:\/\/doi.org\/10.1080\/19439962.2019.1701166","journal-title":"J Transp Safety Secur"},{"issue":"4","key":"5576_CR25","first-page":"86","volume":"5","author":"S Naz","year":"2019","unstructured":"Naz S, Ziauddin S, Shahid AR (2019) Driver fatigue detection using mean intensity, SVM, and SIFT, Int J Interact Multimed. Artif Intell 5(4):86\u201393","journal-title":"Artif Intell"},{"key":"5576_CR26","doi-asserted-by":"crossref","unstructured":"Ozbayoglu M, Kucukayan G, Dogdu E (2017) A Real-time autonomous highway accident detection model based on big data processing and computational intelligence. IEEE, pp. 1807\u20131813.","DOI":"10.1109\/BigData.2016.7840798"},{"issue":"January","key":"5576_CR27","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.aap.2019.05.014","volume":"129","author":"AB Parsa","year":"2019","unstructured":"Parsa AB, Taghipour H, Derrible S, Mohammadian AK (2019) Real-time accident detection: coping with imbalanced data. Accid Anal Prev 129(January):202\u2013210. https:\/\/doi.org\/10.1016\/j.aap.2019.05.014","journal-title":"Accid Anal Prev"},{"key":"5576_CR28","unstructured":"Parsa AB, Kamal K, Taghipour H, Mohammadian AK (2019b) Does security of neighborhoods affect non-mandatory trips? a copula-based joint multinomial-ordinal model of mode and trip distance choices. In: Transportation Research Board 98th Annual Meeting."},{"key":"5576_CR29","unstructured":"Parsa AB, Chauhan RS, Taghipour H, Derrible S, Mohammadian A (Kouros) (2019a) Applying deep learning to detect traffic accidents in real time using spatiotemporal sequential data. arXiv Preprint arXiv 1912.06991."},{"issue":"4","key":"5576_CR30","first-page":"1","volume":"17","author":"A Pashaei","year":"2019","unstructured":"Pashaei A, Ghatee M, Sajedi H (2019) Convolution neural network joint with mixture of extreme learning machines for feature extraction and classification of accident images. J Real-Time Image Process 17(4):1\u201316","journal-title":"J Real-Time Image Process"},{"key":"5576_CR31","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1186\/s12544-018-0316-6","volume":"10","author":"H Razi-Ardakani","year":"2018","unstructured":"Razi-Ardakani H, Mahmoudzadeh A, Kermanshah M (2018) A nested logit analysis of the influence of distraction on types of vehicle crashes. Eur Transp Res Rev 10:44","journal-title":"Eur Transp Res Rev"},{"key":"5576_CR32","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779\u2013788).","DOI":"10.1109\/CVPR.2016.91"},{"key":"5576_CR33","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767."},{"key":"5576_CR34","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems (pp. 91\u201399)."},{"key":"5576_CR35","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510\u20134520).","DOI":"10.1109\/CVPR.2018.00474"},{"key":"5576_CR36","doi-asserted-by":"publisher","first-page":"123112","DOI":"10.1016\/j.physa.2019.123112","volume":"540","author":"MS Sharifi","year":"2019","unstructured":"Sharifi MS, Song Z, Nasr Esfahani H, Christensen K (2019) Exploring heterogeneous pedestrian stream characteristics at walking facilities with different angle intersections. Phys A: Stat Mech Appl 540:123112","journal-title":"Phys A: Stat Mech Appl"},{"key":"5576_CR37","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.trc.2015.03.006","volume":"54","author":"J Sun","year":"2015","unstructured":"Sun J, Sun J (2015) A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data. Transp Res Part C 54:176\u2013186. https:\/\/doi.org\/10.1016\/j.trc.2015.03.006","journal-title":"Transp Res Part C"},{"key":"5576_CR38","doi-asserted-by":"publisher","unstructured":"Thomas RW, Vidal JM (2017) Toward detecting accidents with already available passive traffic information. In: IEEE 7th Annu. Comput. Commun. Work. Conf. 1\u20134. https:\/\/doi.org\/10.1109\/CCWC.2017.7868428.","DOI":"10.1109\/CCWC.2017.7868428"},{"key":"5576_CR39","unstructured":"Traffic Safety Facts FARS, 2013. Natl. Highw. Traffic Saf. Adm. (NHTSA), GES Annu. Rep.Natl. Highw. Traffic Saf. Adm. (NHTSA), GES Annu. Rep."},{"issue":"1","key":"5576_CR40","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s10586-017-0974-5","volume":"21","author":"VCM Vishnu","year":"2018","unstructured":"Vishnu VCM, Nedunchezhian MRR (2018) Intelligent traffic video surveillance and accident detection system with dynamic traffic signal control. Cluster Comput 21(1):135\u2013147. https:\/\/doi.org\/10.1007\/s10586-017-0974-5","journal-title":"Cluster Comput"},{"key":"5576_CR41","doi-asserted-by":"publisher","unstructured":"Xia S, Xiong J, Liu Y, Li G (2015) Vision-based traffic accident detection using matrix approximation. In: 10th Asian Control Conf 1\u20135. https:\/\/doi.org\/10.1109\/ASCC.2015.7244586.","DOI":"10.1109\/ASCC.2015.7244586"},{"key":"5576_CR42","doi-asserted-by":"crossref","unstructured":"Zaldivar J, Calafate CT, Cano JC, Manzoni P (2011) Providing accident detection in vehicular networks through OBD-II devices and android-based smartphones. In: IEEE 36th Conf. Local Comput. Networks. IEEE. pp. 813\u2013819.","DOI":"10.1109\/LCN.2011.6115556"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-05576-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-021-05576-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-05576-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T11:07:27Z","timestamp":1629198447000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-021-05576-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,27]]},"references-count":42,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["5576"],"URL":"https:\/\/doi.org\/10.1007\/s00500-021-05576-w","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,27]]},"assertion":[{"value":"27 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"There is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}