{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T14:18:14Z","timestamp":1784211494287,"version":"3.55.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T00:00:00Z","timestamp":1609891200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T00:00:00Z","timestamp":1609891200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1007\/s00607-020-00869-8","type":"journal-article","created":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T13:06:18Z","timestamp":1609938378000},"page":"211-230","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":116,"title":["An improved YOLO-based road traffic monitoring system"],"prefix":"10.1007","volume":"103","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6956-7641","authenticated-orcid":false,"given":"Mohammed A. A.","family":"Al-qaness","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aaqif Afzaal","family":"Abbasi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rehab Ali","family":"Ibrahim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeed H.","family":"Alsamhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ammar","family":"Hawbani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,6]]},"reference":[{"key":"869_CR1","doi-asserted-by":"crossref","unstructured":"Zhu Y, Wang J, Lu H (2008) A study on urban traffic congestion dynamic predict method based on advanced fuzzy clustering model. In: Proceedings of the 2008 international conference on computational intelligence and security, vol 2, pp 96\u2013100. IEEE.","DOI":"10.1109\/CIS.2008.194"},{"key":"869_CR2","doi-asserted-by":"crossref","unstructured":"De Oliveira MB, Neto ADA (2013) Optimization of traffic lights timing based on multiple neural networks. In: Proceedings of the 2013 IEEE 25th international conference on tools with artificial intelligence, pp 825\u2013832. IEEE.","DOI":"10.1109\/ICTAI.2013.126"},{"key":"869_CR3","unstructured":"Lee HJ, Chen SY, Wang SZ (2004) Extraction and recognition of license plates of motorcycles and vehicles on highways. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, vol 4, pp 356\u2013359. IEEE."},{"issue":"4","key":"869_CR4","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1109\/25.467963","volume":"44","author":"P Comelli","year":"1995","unstructured":"Comelli P, Ferragina P, Granieri MN, Stabile F (1995) Optical recognition of motor vehicle license plates. IEEE Trans Veh Technol 44(4):790\u2013799","journal-title":"IEEE Trans Veh Technol"},{"key":"869_CR5","doi-asserted-by":"crossref","unstructured":"Dharamadhat T, Thanasoontornlerk K, Kanongchaiyos P (2009) Tracking object in video pictures based on background subtraction and image matching. In: Proceedings of the 2008 IEEE international conference on robotics and biomimetics, pp 1255\u20131260. IEEE.","DOI":"10.1109\/ROBIO.2009.4913180"},{"key":"869_CR6","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1007\/978-3-642-21593-3_42","volume-title":"International conference image analysis and recognition","author":"B Cancela","year":"2011","unstructured":"Cancela B, Ortega M, Penedo MG, Fern\u00e1ndez A (2011) Solving multiple-target tracking using adaptive filters. International conference image analysis and recognition. Springer, Berlin, Heidelberg, pp 416\u2013425"},{"key":"869_CR7","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1007\/3-540-44690-7_22","volume-title":"International workshop on robot vision","author":"A McIvor","year":"2001","unstructured":"McIvor A, Zang Q, Klette R (2001) The background subtraction problem for video surveillance systems. International workshop on robot vision. Springer, Berlin, Heidelberg, pp 176\u2013183"},{"issue":"3","key":"869_CR8","first-page":"15","volume":"5","author":"G Sekar","year":"2015","unstructured":"Sekar G, Deepika M (2015) Complex background subtraction using kalman filter. Int J Eng Res Appl 5(3):15\u201320","journal-title":"Int J Eng Res Appl"},{"issue":"1","key":"869_CR9","first-page":"37","volume":"3","author":"H Rabiu","year":"2013","unstructured":"Rabiu H (2013) Vehicle detection and classification for cluttered urban intersection. Int J Comput Sci Eng Appl 3(1):37","journal-title":"Int J Comput Sci Eng Appl"},{"key":"869_CR10","doi-asserted-by":"crossref","unstructured":"Wang K, Liang Y, Xing X, Zhang R (2015) Target detection algorithm based on gaussian mixture background subtraction model. In: Proceedings of the 2015 Chinese intelligent automation conference, pp 439\u2013447. Springer, Berlin, Heidelberg.","DOI":"10.1007\/978-3-662-46469-4_47"},{"key":"869_CR11","first-page":"1221","volume-title":"Applied mechanics and materials","author":"M Yazdi","year":"2014","unstructured":"Yazdi M, Bagherzadeh MA, Jokar M, Abasi MA (2014) Block-wise background subtraction based on gaussian mixture models. Applied mechanics and materials, vol 490. Trans Tech Publications Ltd, Switzerland, pp 1221\u20131227"},{"issue":"1","key":"869_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1049\/iet-its.2011.0019","volume":"6","author":"YM Chan","year":"2012","unstructured":"Chan YM, Huang SS, Fu LC, Hsiao PY, Lo MF (2012) Vehicle detection and tracking under various lighting conditions using a particle filter. IET Intell Transp Syst 6(1):1\u20138","journal-title":"IET Intell Transp Syst"},{"issue":"2","key":"869_CR13","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1109\/TITS.2012.2187894","volume":"13","author":"HT Niknejad","year":"2012","unstructured":"Niknejad HT, Takeuchi A, Mita S, McAllester D (2012) On-road multivehicle tracking using deformable object model and particle filter with improved likelihood estimation. IEEE Trans Intell Transp Syst 13(2):748\u2013758","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"5","key":"869_CR14","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1109\/JSTARS.2013.2273871","volume":"7","author":"T Long","year":"2013","unstructured":"Long T, Jiao W, He G, Wang W (2013) Automatic line segment registration using Gaussian mixture model and expectation-maximization algorithm. IEEE J Sel Top Appl Earth Obs Remote Sens 7(5):1688\u20131699","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"issue":"2","key":"869_CR15","first-page":"89","volume":"24","author":"YL Chen","year":"2009","unstructured":"Chen YL, Wu BF, Lin CT, Fan CJ, Hsieh CM (2009) Real-time vision-based vehicle detection and tracking on a moving vehicle for nighttime driver assistance. Int J Robot Autom 24(2):89\u2013102","journal-title":"Int J Robot Autom"},{"issue":"7","key":"869_CR16","doi-asserted-by":"publisher","first-page":"2019","DOI":"10.1109\/TIP.2006.877062","volume":"15","author":"Z Sun","year":"2006","unstructured":"Sun Z, Bebis G, Miller R (2006) Monocular precrash vehicle detection: features and classifiers. IEEE Trans Image Process 15(7):2019\u20132034","journal-title":"IEEE Trans Image Process"},{"key":"869_CR17","unstructured":"Junior OL, Nunes U (2008) Improving the generalization properties of neural networks: an application to vehicle detection. In Proceedings of the 2008 11th international IEEE conference on intelligent transportation systems, pp 310\u2013315. IEEE."},{"issue":"19","key":"869_CR18","doi-asserted-by":"publisher","first-page":"7941","DOI":"10.1016\/j.ijleo.2016.05.092","volume":"127","author":"G Yan","year":"2016","unstructured":"Yan G, Yu M, Yu Y, Fan L (2016) Real-time vehicle detection using histograms of oriented gradients and AdaBoost classification. Optik 127(19):7941\u20137951","journal-title":"Optik"},{"key":"869_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2008\/782432","volume":"2008","author":"P Negri","year":"2008","unstructured":"Negri P, Clady X, Hanif SM, Prevost L (2008) A cascade of boosted generative and discriminative classifiers for vehicle detection. EURASIP J Adv Signal Process 2008:1\u201312","journal-title":"EURASIP J Adv Signal Process"},{"key":"869_CR20","doi-asserted-by":"crossref","unstructured":"Withopf D, Jahne B (2006) Learning algorithm for real-time vehicle tracking. In: Proceedings of the 2006 IEEE intelligent transportation systems conference, pp 516\u2013521. IEEE.","DOI":"10.1109\/ITSC.2006.1706793"},{"issue":"1","key":"869_CR21","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MRA.2005.1411419","volume":"12","author":"SC Chen","year":"2005","unstructured":"Chen SC, Shyu ML, Peeta S, Zhang C (2005) Spatiotemporal vehicle tracking: the use of unsupervised learning-based segmentation and object tracking. IEEE Robot Autom Mag 12(1):50\u201358","journal-title":"IEEE Robot Autom Mag"},{"key":"869_CR22","doi-asserted-by":"crossref","unstructured":"Uy ACP, Quiros ARF, Bedruz RA, Abad A, Bandala A, Sybingco E, Dadios EP (2016) Automated traffic violation apprehension system using genetic algorithm and artificial neural network. In: Proceedings of the 2016 IEEE region 10 conference (TENCON), pp 2094\u20132099. IEEE.","DOI":"10.1109\/TENCON.2016.7848395"},{"key":"869_CR23","unstructured":"City Brain project (2016). https:\/\/www.alibabacloud.com\/solutions\/intelligence-brain\/city. Accessed 26 Jan 2020"},{"key":"869_CR24","unstructured":"Vivacity traffic management system (2016). https:\/\/vivacitylabs.com\/technology\/. Accessed 26 Jan 2020"},{"key":"869_CR25","unstructured":"Traffic Congestion Survey (2015). https:\/\/www.reuters.com\/article\/us-usa-traffic-study\/u-s-commuters-spend-about-42-hours-a-year-stuck-in-traffic-jams-idUSKCN0QV0A820150826, 2015. Accessed 26 Jan 2020."},{"issue":"4","key":"869_CR26","first-page":"329","volume":"54","author":"T Dutta","year":"2010","unstructured":"Dutta T, Pal G (2010) Pulmonary function test in traffic police personnel in Pondicherry. Indian J Physiol Pharmacol 54(4):329\u2013336","journal-title":"Indian J Physiol Pharmacol"},{"key":"869_CR27","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767."},{"key":"869_CR28","doi-asserted-by":"crossref","unstructured":"Hilario CH, Collado JM, Armingol JM, De La Escalera A (2005) Pyramidal image analysis for vehicle detection. In: Proceedings of the IEEE intelligent vehicles symposium, 2005, pp 88\u201393. IEEE.","DOI":"10.1109\/IVS.2005.1505083"},{"key":"869_CR29","doi-asserted-by":"crossref","unstructured":"Sun D, Roth S, Black MJ (2010) Secrets of optical flow estimation and their principles. In: Proceedings of the 2010 IEEE computer society conference on computer vision and pattern recognition, pp 2432\u20132439. IEEE.","DOI":"10.1109\/CVPR.2010.5539939"},{"key":"869_CR30","doi-asserted-by":"crossref","unstructured":"Kim G, Kim H, Park J, Yu Y (2011) Vehicle tracking based on kalman filter in tunnel. In: Proceedings of the international conference on information security and assurance, pp 250\u2013256. Springer, Berlin, Heidelberg.","DOI":"10.1007\/978-3-642-23141-4_24"},{"issue":"2","key":"869_CR31","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/78.978396","volume":"50","author":"F Gustafsson","year":"2002","unstructured":"Gustafsson F, Gunnarsson F, Bergman N, Forssell U, Jansson J, Karlsson R, Nordlund PJ (2002) Particle filters for positioning, navigation, and tracking. IEEE Trans Signal Process 50(2):425\u2013437","journal-title":"IEEE Trans Signal Process"},{"key":"869_CR32","doi-asserted-by":"crossref","unstructured":"Exner D, Bruns E, Kurz D, Grundh\u00f6fer A, Bimber O (2010) Fast and robust CAMShift tracking. In: Proceedings of the 2010 IEEE computer society conference on computer vision and pattern recognition-workshops, pp 9\u201316. IEEE.","DOI":"10.1109\/CVPRW.2010.5543787"},{"key":"869_CR33","doi-asserted-by":"crossref","unstructured":"Bewley A, Ge Z, Ott L, Ramos F, Upcroft B (2016) Simple online and realtime tracking. In: Proceedings of the 2016 IEEE international conference on image processing (ICIP), pp 3464\u20133468. IEEE.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"869_CR34","unstructured":"Open Images Dataset. https:\/\/storage.googleapis.com\/openimages\/web\/index.html. Accessed 11 March 2020"},{"key":"869_CR35","unstructured":"COCO Dataset. http:\/\/cocodataset.org. Accessed 20 Feb 2020."},{"key":"869_CR36","unstructured":"Pascal VOC Dataset. http:\/\/host.robots.ox.ac.uk\/pascal\/VOC\/. Accessed 14 March 2020."},{"key":"869_CR37","unstructured":"Stanford Cars Dataset. https:\/\/ai.stanford.edu\/~jkrause\/cars\/car_dataset.html. Accessed 26 Feb 2020."},{"issue":"2","key":"869_CR38","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1109\/TITS.2010.2040177","volume":"11","author":"S Sivaraman","year":"2010","unstructured":"Sivaraman S, Trivedi MM (2010) A general active-learning framework for on-road vehicle recognition and tracking. IEEE Trans Intell Transp Syst 11(2):267\u2013276","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"869_CR39","doi-asserted-by":"crossref","unstructured":"Nguyen A, Yosinski J, Clune J (2015) Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 427\u2013436.","DOI":"10.1109\/CVPR.2015.7298640"},{"issue":"1","key":"869_CR40","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"issue":"7","key":"869_CR41","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.3390\/s20072025","volume":"20","author":"A Kulikajevas","year":"2020","unstructured":"Kulikajevas A, Maskeliunas R, Damasevicius R, Ho ES (2020) 3D object reconstruction from imperfect depth data using extended YOLOv3 network. Sensors 20(7):2025","journal-title":"Sensors"},{"issue":"18","key":"869_CR42","doi-asserted-by":"publisher","first-page":"3781","DOI":"10.3390\/app9183781","volume":"9","author":"Y Li","year":"2019","unstructured":"Li Y, Han Z, Xu H, Liu L, Li X, Zhang K (2019) YOLOv3-lite: a lightweight crack detection network for aircraft structure based on depthwise separable convolutions. Appl Sci 9(18):3781","journal-title":"Appl Sci"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-020-00869-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00607-020-00869-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-020-00869-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T16:42:02Z","timestamp":1613839322000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00607-020-00869-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,6]]},"references-count":42,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["869"],"URL":"https:\/\/doi.org\/10.1007\/s00607-020-00869-8","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"value":"0010-485X","type":"print"},{"value":"1436-5057","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,6]]},"assertion":[{"value":"24 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}