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Advanced framework for microscopic and lane‐level macroscopic traffic parameters estimation from UAV video

dc.contributor.authorKe, Ruimin
dc.contributor.authorFeng, Shuo
dc.contributor.authorCui, Zhiyong
dc.contributor.authorWang, Yinhai
dc.date.accessioned2021-02-04T21:54:57Z
dc.date.available2021-08-04 16:54:55en
dc.date.available2021-02-04T21:54:57Z
dc.date.issued2020-07
dc.identifier.citationKe, Ruimin; Feng, Shuo; Cui, Zhiyong; Wang, Yinhai (2020). "Advanced framework for microscopic and lane‐level macroscopic traffic parameters estimation from UAV video." IET Intelligent Transport Systems 14(7): 724-734.
dc.identifier.issn1751-956X
dc.identifier.issn1751-9578
dc.identifier.urihttps://hdl.handle.net/2027.42/166282
dc.publisherThe Institution of Engineering and Technology
dc.publisherWiley Periodicals, Inc.
dc.subject.othermicroscopic lane‐level macroscopic traffic parameters estimation
dc.subject.othermodern traffic sensing research
dc.subject.otherUAV‐based traffic sensing
dc.subject.otheraggregated macroscopic traffic data
dc.subject.otherhigher‐resolution traffic data
dc.subject.othertraffic patterns
dc.subject.othertraffic flow characteristics
dc.subject.otherreal‐world UAV video data
dc.subject.otheradvanced traffic management
dc.subject.otherUAV video features
dc.subject.other(B6135) Optical, image and video signal processing
dc.subject.other(C3390C) Mobile robots
dc.subject.other(C5260B) Computer vision and image processing techniques
dc.subject.other(C5260D) Video signal processing
dc.subject.other(C7445) Traffic engineering computing
dc.subject.otherparameter estimation
dc.subject.otherautonomous aerial vehicles
dc.subject.otherroad vehicles
dc.subject.otherroad traffic
dc.subject.otherobject detection
dc.subject.othertraffic engineering computing
dc.subject.othervideo signal processing
dc.subject.otherrobot vision
dc.subject.otheradvanced traffic sensing
dc.subject.otheraggregated macroscopic traffic parameters
dc.titleAdvanced framework for microscopic and lane‐level macroscopic traffic parameters estimation from UAV video
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelComputer Science
dc.subject.hlbtoplevelEngineering
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/166282/1/itr2bf00873.pdf
dc.identifier.doi10.1049/iet-its.2019.0463
dc.identifier.doihttps://dx.doi.org/10.7302/205
dc.identifier.sourceIET Intelligent Transport Systems
dc.identifier.citedreferenceTeutsch M. Krüger W.: ‘ Detection, segmentation, and tracking of moving objects in UAV videos ’. 2012 IEEE Ninth Int. Conf. on Advanced Video and Signal‐Based Surveillance, Beijing, people’s Republic of China, 2012, pp. 313 – 318
dc.identifier.citedreferenceBarmpounakis E.N. Vlahogianni E.I. Golias J.C.: ‘ Unmanned aerial aircraft systems for transportation engineering: current practice and future challenges ’, Int. J. Transp. Sci. Technol., 2016, 5, ( 3 ), pp. 111 – 122
dc.identifier.citedreferenceKanistras K. Martins G. Rutherford M.J. et al.: ‘ Survey of unmanned aerial vehicles (UAVs) for traffic monitoring ’, in Valavanis Kimon P. Vachtsevanos George J. (Eds.): ‘ Handbook of unmanned aerial vehicles ’ ( Springer, USA 2015 ), pp. 2643 – 2666
dc.identifier.citedreferenceDu Y. Zhao C. Li F. et al.: ‘ An open data platform for traffic parameters measurement via multirotor unmanned aerial vehicles video ’, J. Adv. Transp., 2017, 2017, pp. 1 – 12
dc.identifier.citedreferenceCoifman B. McCord M. Mishalani R.G. et al.: ‘ Surface transportation surveillance from unmanned aerial vehicles ’. Proc. of the 83rd Annual Meeting of the Transportation Research Board, Washington, DC, USA, 2004
dc.identifier.citedreferenceAngel A. Hickman M. Mirchandani P. et al.: ‘ Methods of analyzing traffic imagery collected from aerial platforms ’, IEEE Trans. Intell. Transp. Syst., 2003, 4, ( 2 ), pp. 99 – 107
dc.identifier.citedreferenceZhou H. Kong H. Wei L. et al.: ‘ Efficient road detection and tracking for unmanned aerial vehicle ’, IEEE Trans. Intell. Transp. Syst., 2015, 16, ( 1 ), pp. 297 – 309
dc.identifier.citedreferenceFreeman B.S. Al Matawah J.A. Al Najjar M. et al.: ‘ Vehicle stacking estimation at signalized intersections with unmanned aerial systems ’, Int. J. Transp. Sci. Technol., 2019, 8, pp. 231 – 249
dc.identifier.citedreferenceKe R. Lutin J. Spears J. et al.: ‘ A cost‐effective framework for automated vehicle‐pedestrian near‐miss detection through onboard monocular vision ’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA, 2017
dc.identifier.citedreferenceKe R. Pan Z. Pu Z. et al.: ‘ Roadway surveillance video camera calibration using standard shipping container ’. 2017 Int. Smart Cities Conf. (ISC2), Wuxi, People’s Republic of China, 2017, pp. 1 – 6
dc.identifier.citedreferenceMcCord M. Yang Y. Jiang Z. et al.: ‘ Estimating annual average daily traffic from satellite imagery and air photos: empirical results ’, Transp. Res. Rec. J. Transp. Res. Board, 2003, 1855, pp. 136 – 142
dc.identifier.citedreferenceSalvo G. Caruso L. Scordo A.: ‘ Urban traffic analysis through an UAV ’, Proc. Soc. Behav. Sci., 2014, 111, pp. 1083 – 1091
dc.identifier.citedreferenceKhan M.A. Ectors W. Bellemans T. et al.: ‘ Unmanned aerial vehicle–based traffic analysis: methodological framework for automated multivehicle trajectory extraction ’, Transp. Res. Rec. J. Transp. Res. Board, 2017, 2626, pp. 25 – 33
dc.identifier.citedreferenceKaufmann S. Kerner B.S. Rehborn H. et al.: ‘ Aerial observations of moving synchronized flow patterns in over‐saturated city traffic ’, Transp. Res. C, Emerg. Technol., 2018, 86, pp. 393 – 406
dc.identifier.citedreferenceCao X. Wu C. Lan J. et al.: ‘ Vehicle detection and motion analysis in low‐altitude airborne video under urban environment ’, IEEE Trans. Circuits Syst. Video Technol., 2011, 21, ( 10 ), pp. 1522 – 1533
dc.identifier.citedreferenceAmmour N. Alhichri H. Bazi Y. et al.: ‘ Deep learning approach for car detection in UAV imagery ’, Remote Sens., 2017, 9, ( 4 ), p. 312
dc.identifier.citedreferenceXu Y. Yu G. Wu X. et al.: ‘ An enhanced Viola‐Jones vehicle detection method from unmanned aerial vehicles imagery ’, IEEE Trans Intell. Transp. Syst., 2017, 18, ( 7 ), pp. 1845 – 1856
dc.identifier.citedreferenceShastry A.C. Schowengerdt R.A.: ‘ Airborne video registration and traffic‐flow parameter estimation ’, IEEE Trans. Intell. Transp. Syst., 2005, 6, ( 4 ), pp. 391 – 405
dc.identifier.citedreferenceCao X. Gao C. Lan J. et al.: ‘ Ego motion guided particle filter for vehicle tracking in airborne videos ’, Neurocomputing, 2014, 124, pp. 168 – 177
dc.identifier.citedreferenceKe R. Kim S. Li Z. et al.: ‘ Motion‐vector clustering for traffic speed detection from UAV video ’. 2015 IEEE First Int. Smart Cities Conf. (ISC2), Guadalajara, Mexico, 2015, pp. 1 – 5
dc.identifier.citedreferenceKe R.: ‘ A novel framework for real‐time traffic flow parameter estimation from aerial videos ’. 2016
dc.identifier.citedreferenceKe R. Li Z. Kim S. et al.: ‘ Real‐time bidirectional traffic flow parameter estimation from aerial videos ’, IEEE Trans. Intell. Transp. Syst., 2017, 18, ( 4 ), pp. 890 – 901
dc.identifier.citedreferenceChen P. Zeng W. Yu G. et al.: ‘ Surrogate safety analysis of pedestrian‐vehicle conflict at intersections using unmanned aerial vehicle videos ’, J. Adv. Transp., 2017, 2017, pp. 1 – 12
dc.identifier.citedreferenceKe R. Li Z. Tang J. et al.: ‘ Real‐time traffic flow parameter estimation from UAV video based on ensemble classifier and optical flow ’, IEEE Trans. Intell. Transp. Syst., 2018, 99, pp. 1 – 11
dc.identifier.citedreferenceLi J. Chen S. Zhang F. et al.: ‘ An adaptive framework for multi‐vehicle ground speed estimation in airborne videos ’, Remote Sens., 2019, 11, ( 10 ), p. 1241
dc.identifier.citedreferenceBarmpounakis E.N. Vlahogianni E.I. Golias J.C. et al.: ‘ How accurate are small drones for measuring microscopic traffic parameters? ’, Transp. Lett., 2019, 11, pp. 332 – 340
dc.identifier.citedreferenceKim E.‐J. Park H.‐C. Ham S.‐W. et al.: ‘ Extracting vehicle trajectories using unmanned aerial vehicles in congested traffic conditions ’, J. Adv. Transp., 2019, 2019, 16 pages
dc.identifier.citedreferenceFeng S. Wang X. Sun H. et al.: ‘ A better understanding of long‐range temporal dependence of traffic flow time series ’, Phys. A Stat. Mech. Appl., 2018, 492, pp. 639 – 650
dc.identifier.citedreferenceRodríguez‐Canosa G.R. Thomas S. Del Cerro J. et al.: ‘ A real‐time method to detect and track moving objects (DATMO) from unmanned aerial vehicles (UAVs) using a single camera ’, Remote Sens., 2012, 4, ( 4 ), pp. 1090 – 1111
dc.identifier.citedreferenceTsao P. Ik T.‐U. Chen G.‐W. et al.: ‘ Stitching aerial images for vehicle positioning and tracking ’. 2018 IEEE Int. Conf. on Data Mining Workshops (ICDMW), Singapore, 2018, pp. 616 – 623
dc.identifier.citedreferenceBreckon T.P. Barnes S.E. Eichner M.L. et al.: ‘ Autonomous real‐time vehicle detection from a medium‐level UAV ’. Proc. 24th Int. Conf. on Unmanned Air Vehicle Systems, Bristol, UK, 2009, pp. 21 – 29
dc.identifier.citedreferenceGomaa A. Abdelwahab M.M. Abo‐Zahhad M.: ‘ Real‐time algorithm for simultaneous vehicle detection and tracking in aerial view videos ’. 2018 IEEE 61st Int. Midwest Symp. on Circuits and Systems (MWSCAS), Windsor, Canada, 2018, pp. 222 – 225
dc.identifier.citedreferenceNajiya K.V Archana M.: ‘ UAV video processing for traffic surveillance with enhanced vehicle detection ’. 2018 Second Int. Conf. on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, 2018, pp. 662 – 668
dc.identifier.citedreferenceLi J. Ye D.H. Chung T. et al.: ‘ Multi‐target detection and tracking from a single camera in unmanned aerial vehicles (UAVs) ’. 2016 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Daejeon, Republic of Korea, 2016, pp. 4992 – 4997
dc.identifier.citedreferenceCarletti V. Greco A. Saggese A. et al.: ‘ Multi‐object tracking by flying cameras based on a forward‐backward interaction ’, IEEE Access, 2018, 6, pp. 43905 – 43919
dc.identifier.citedreferenceDu D. Qi Y. Yu H. et al.: ‘ The unmanned aerial vehicle benchmark: object detection and tracking ’. Proc. of the European Conf. on Computer Vision (ECCV), Munich, Germany, 2018, pp. 370 – 386
dc.identifier.citedreferenceKhan M. Ectors W. Bellemans T. et al.: ‘ Unmanned aerial vehicle‐based traffic analysis: a case study for shockwave identification and flow parameters estimation at signalized intersections ’, Remote Sens., 2018, 10, ( 3 ), p. 458
dc.identifier.citedreferenceZhu J. Sun K. Jia S. et al.: ‘ Urban traffic density estimation based on ultrahigh‐resolution UAV video and deep neural network ’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2018, 11, ( 12 ), pp. 4968 – 4981
dc.identifier.citedreferenceBewley A. Ge Z. Ott L. et al.: ‘ Simple online and realtime tracking ’. 2016 IEEE Int. Conf. on Image Processing (ICIP), Phoenix, AZ, USA, 2016, pp. 3464 – 3468
dc.identifier.citedreferenceLucas B.D. Kanade T. et al.: ‘ An iterative image registration technique with an application to stereo vision ’, 1981
dc.identifier.citedreferenceCanny J.: ‘ A computational approach to edge detection ’, IEEE Trans. Pattern Anal. Mach. Intell., 1986, PAMI‐8, ( 6 ), pp. 679 – 698
dc.identifier.citedreferenceDuda R.O. Hart P.E.: ‘ Use of the Hough transformation to detect lines and curves in pictures ’, 1971
dc.identifier.citedreferenceEster M. Kriegel H.‐P. Sander J. et al.: ‘ A density‐based algorithm for discovering clusters in large spatial databases with noise ’. Knowledge Discovery and Data Mining (KDD), Portland, OR, USA, 1996, pp. 226 – 231
dc.working.doi10.7302/205en
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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