Tài liệu hạn chế xem trước, để xem đầy đủ mời bạn chọn Tải xuống
1
/ 157 trang
THÔNG TIN TÀI LIỆU
Thông tin cơ bản
Định dạng
Số trang
157
Dung lượng
7,43 MB
Nội dung
BáGI ODệCV OT O TRìNG I HC B CH KHOA H NáI NGUY N VI T HìNG NGHI NCUPH TTRI NC CGI IPH P GI M S T L×U L×ĐNG V QU N Lị PHìèNG TI N GIAO THNG QUA CAMERA GI M S T NG NH:KßTHU T I NTÛ M Să: 9520203 LU N NTI NS KòTHU T I NTÛ NG×˝I HײNG D N KHOA H¯C PGS.TS NGUY N TI N DƠNG H N¸I - 2020 B¸GI ODƯCV OT O TRìNG I HC B CH KHOA H NáI NGUY N VI T H×NG NGHI NCÙUPH TTRI NC CGI IPH P GI M S T L×U L×ĐNG V QU N Lị PHìèNG TI N GIAO THNG QUA CAMERA GI M S T NG NH:KòTHU T I NT M Să: 9520203 LU N NTI NS KòTHU T I NT NGìI HìNG D N KHOA H¯C PGS TS NGUY N TI N DƠNG H N¸I - 2020 L˝I CAM OAN Tỉi xin cam oan c¡c k‚t qu£ tr…nh b y Lu“n Ăn l cổng trnh nghiản cứu ca tổi dữợi sỹ hữợng dÔn ca cĂn b hữợng dÔn sut thới gian l m Nghi¶n cøu sinh C¡c k‚t qu£ nghi¶n cøu ÷ỉc tr…nh b y Lu“n ¡n l ho n to n trung thüc, kh¡ch quan v ch÷a tłng ÷æc c¡c t¡c gi£ kh¡c cæng bŁ C¡c k‚t qu£ sò dửng tham khÊo u  ữổc trch dÔn y ı v theo óng quy ành Lu“n ¡n H Ni, ng y 30 thĂng 01 nôm 2020 Ngữới hữợng dÔn khoa hồc PGS TS Nguyn Tin Dụng TĂc giÊ Nguyn Viằt Hững LIC MèN u tiản, tổi xin b y tọ lặng bit ỡn chƠn th nh v sƠu sc ca mnh tợi thy hữợng dÔn khoa hồc PGS TS Nguyn Tin Dụng Thy  nh hữợng cho tổi trin khai cĂc ỵ tững khoa hồc, luổn tn tnh hữợng dÔn tổi sut thới gian hồc nghiản cứu v thy  d nh nhiu thới gian tƠm huy‚t, hØ trỉ tỉi v• måi m°t ” ho n th nh lu“n ¡n n y Tỉi cơng xin ch¥n th nh c£m ìn c¡c thƒy, c¡c cỉ, c¡c anh ch em Nghiản cứu sinh B mổn iằn tò v Kÿ thu“t M¡y t‰nh, Vi»n i»n tß - Vi„n thổng trữớng i hồc BĂch Khoa H Ni  to iu kiằn, giúp ù v hữợng dÔn tổi quĂ trnh hồc v nghiản cứu ti trữớng Tổi xin trƠn trồng cÊm ỡn LÂnh o trữớng i hồc BĂch Khoa H Ni, Phặng o to, Viằn iằn tò - Vin thổng  to mồi iu kiằn thun lổi nhĐt cho nghi¶n cøu sinh suŁt qu¡ tr…nh håc t“p v nghi¶n cøu V tỉi cơng xin c£m ìn TS Nguyn Tin Hặa v TS T Th Kim Huằ  gióp ï tỉi tr…nh b y Lu“n ¡n CuŁi cũng, tổi xin b y tọ lặng bit ỡn tợi gia nh tổi: b mà hai bản, vổ v em gĂi tổi  luổn ng viản khch lằ v tinh thƒn v v“t ch§t ” tỉi câ ºng lüc cỉng vi»c v nghi¶n cøu khoa håc H Nºi, ng y 30 th¡ng 01 n«m 2020 T¡c gi£ Nguy„n Vi»t H÷ng MƯC LƯC Trang DANH MƯC C C CHÚ VI T T T DANH MÖC C C H NH V , iii ˙ THÀ v DANH MÖC C C B NG viii DANH MƯC C C KÞ HI U TO N H¯C M— ix U CH×ÌNG T˚NG QUAN C C V N CÕA XÛ LÞ TRONG GIAO TH˘NG HÉN HĐP NH 18 1.1 Giỵi thi»u 18 1.2 H» thŁng giao thæng thæng minh 18 1.2.1 Chøc n«ng 19 1.2.2 Nhi»m vö 21 1.2.3 Kàch b£n sß dưng 22 1.3 Tim nông ứng dửng ca xò lỵ Ênh ITS 22 1.3.1 Ph¡t hi»n ph÷ìng ti»n 22 1.3.2 C¡c øng dưng xß lỵ Ênh 23 1.3.3 Quy trnh ứng dửng xò lỵ Ênh giao thæng 24 1.4 Hiằn trng xò lỵ Ênh ITS 25 1.4.1 KhÊ nông ca xò lỵ Ênh 26 1.4.2 CĂc thĂch thức xò lỵ Ênh ITS 26 1.5 CĂc chức nông chnh ca xò lỵ Ênh ITS 28 1.5.1 Nh“n d⁄ng bi”n sŁ ph÷ìng ti»n 28 1.5.2 PhƠn loi phữỡng tiằn 31 1.5.3 o tŁc º ph÷ìng ti»n 41 1.5.4 Ph¥n t‰ch l÷u l÷ỉng ph÷ìng ti»n 46 1.6 C¡c v§n ca xò lỵ Ênh giao thổng hỉn hổp 54 1.7 K‚t lu“n ch÷ìng 55 i ii CHìèNG QU N Lị PH×ÌNG TI N GIAO TH˘NG 57 2.1 Giỵi thi»u 57 2.2 Hằ thng quÊn lỵ phữỡng tiằn giao thæng 57 2.2.1 C£i thiằn chĐt lữổng Ênh 57 2.2.2 Nh“n di»n ph÷ìng ti»n 59 2.2.3 PhƠn loi phữỡng tiằn 62 2.2.4 o tŁc º ph÷ìng ti»n giao thỉng 64 2.3 xuĐt thut toĂn cÊi thiằn v nƠng cao chĐt lữổng Ênh 65 2.4 xuĐt phữỡng phĂp phƠn loi phữỡng tiằn giao thổng 74 2.5 xuĐt mổ hnh hõa phữỡng ph¡p o tŁc º ph÷ìng ti»n 79 2.6 K‚t lu“n ch÷ìng 90 CH×ÌNG L×U L×ĐNG PH×ÌNG TI N GIAO TH˘NG 91 3.1 Giỵi thi»u 91 3.2 H» thŁng gi¡m s¡t l÷u l÷ỉng ph÷ìng ti»n giao thỉng 91 3.3 GiĂm sĂt v iu khin dặng phữỡng tiằn giao thỉng theo m“t º l÷u l÷ỉng 92 3.3.1 xuĐt giĂm sĂt v iu khin dặng ph÷ìng ti»n giao thỉng theo m“t º l÷u l÷ỉng 95 3.4 Gi¡m s¡t v iu khin dặng phữỡng tiằn giao thổng theo s l÷ỉng ph÷ìng ti»n v chıng lo⁄i ph÷ìng ti»n 109 3.4.1 xuĐt giĂm sĂt lữu lữổng phữỡng tiằn theo s l÷ỉng v chıng lo⁄i ph÷ìng ti»n 112 3.5 K‚t lu“n ch÷ìng 123 K T LU N DANH MệC C C CNG TR NH 124 CNG Bă CếA LU N N 126 T I LI U THAM KH O 128 PHÖ LÖC A 139 DANHMÖCC CCHÚVI TT T AI Active Infrared Hỗng ngoi ch ng ALPR Automated License Plate Recognition Nh“n d⁄ng bi”n sŁ tü ºng AS Acoustic Sensor CÊm bin Ơm BA Block Artifact nh hững tł c¡c khŁi l¥n c“n BI Bicubic Interpolation Gi£i thu“t nºi suy Bicubic CCD Charge Coupled Device Linh ki»n t‰ch i»n k†p CI Cubic Interpolation Gi£i thu“t nºi suy Cubic CW Continuous Wave Sâng li¶n tưc DA Direction Angle Gâc trüc ti‚p DWT Discrete Wavelet Transform Chuy”n Œi sâng ríi r⁄c FLC Fuzzy Logic Controller Bº i•u khi”n logic mí FMCWR frequency Modulated Continous Waves RADAR i•u ch‚ tƒn sŁ sâng RADAR li¶n ti‚p FPS Frame per Second Khung hnh trản giƠy GSM Global System for Mobile Communications Hằ thŁng thæng tin di ºng to n cƒu ILDS Inductive Loop Detector System H» thŁng nh“n di»n b‹ng vỈng l°p cÊm ứng t IP Image Processing Xò lỵ Ênh ITS Intelligent Transportation System Giao thæng Thæng minh kNN k Nearest Neighbor k lƠn cn gn nhĐt Khuych i Ănh sĂng b‹ng LASER Light Amplification by Stimulated Emission of Radiation LED Light Emitting Diode iii ph¡t x⁄ k‰ch th‰ch Diode ph¡t quang iv MD Magnetic Detector ƒu dỈ tł t‰nh OCR Optical Character Recognization Nhn dng kỵ tỹ quang hồc PI Passive Infrared Hỗng ngoi b ng PSNR Peak Signal to Noise Ratio T¿ sŁ t‰n hi»u cüc ⁄i tr¶n nhi„u RADAR RAdio Detection And Ranging DỈ t…m v ành b‹ng sâng væ tuy‚n RFID Radio Frequency IDentification X¡c thüc tƒn sŁ sâng væ tuy‚n SIFT Scale-Invariant Feature Transform Bi‚n Œi °c tr÷ng t l» khỉng Œi SVM Support Vector Machine V†c-tì m¡y hØ trỉ TMS Traffic Monitoring System H» thŁng gi¡m s¡t giao thæng US Ultrasonic Sensor C£m bi‚n siảu Ơm VANET Vehicular Ad Hoc Networks Mng xe c b§t ành VP Vanish Point i”m khu§t 125 ( 1; m v n ), v L tữỡng ứng vợi vũng c¡c ph÷ìng ti»n dłng chí –n ä, phƒn ÷íng giao ca nút giao thổng v vũng luỗng phữỡng tiằn s‡ di chuy”n v o Tł vi»c gi¡m s¡t c¡c tham sŁ n y câ th” t‰nh to¡n thíi gian –n giao thỉng ho⁄t ºng cho phò hỉp Lu“n ¡n xuĐt cĂc hữợng phĂt trin tip theo nhữ sau: Nghiản cứu tnh toĂn xuĐt cĂc phữỡng phĂp nƠng cao chĐt lữổng ni suy Ênh theo hữợng Super-Resoulution cÊi thiằn chĐt lữổng Ênh thu ữổc t camera giao thổng Nghiản cứu tnh toĂn o tc phữỡng tiằn t xa trản ữớng cao tc, xĂc nh thới gian bt hnh (shutter time) ca camera Ênh hững tợi thới gian cıa khung h…nh k‚ ti‚p nh‹m möc ‰ch x¡c ành thíi gian b›t h…nh v thíi gian cỈn l⁄i ca mt khung hnh PhĂt trin giÊi phĂp quÊn lỵ v xò lỵ d liằu ca mt hằ thng camera nhm mửc ch ỗng b hoĂ d liằu v ỗng b iu khin dặng phữỡng tiằn DANH MệC C C CNG TR NH CNG Bă CếA LU N N I.C CC˘NGTRNHLI NQUANTRÜCTI P NLU N N NG Conferences: [C1 ] Nguyen Viet Hung, Nguyen Hoang Dung, Le Chung Tran, Thang Manh Hoang & Nguyen Tien Dzung (2016); Vehicle Classification by Estimation of the Direction Angle in a Mixed Traffic Flow, IEEE International Conference on Communications and Electronics (IEEE - ICCE), pp 365 - 368 [C2 ] Nguyen Viet Hung, Nguyen Hoang Dung, Le Chung Tran, Thang Manh Hoang & Nguyen Tien Dzung (2016); A Traffic Monitoring System for a Mixed Traffic Flow Via Road Estimation and Analysis, IEEE International Conference on Communications and Electronics (IEEE - ICCE), pp 375 - 378 [C3 ] Nguyen Viet Hung, Nguyen Thi Thu Hien, Phan Thanh Vinh, Nguyen Thi Thao & Nguyen Tien Dzung (2017); An Utilization of Edge Detection in a Modified Bicubic Interpolation Used for Frame Enhancement in a Camera-based Traffic Monitoring, IEEE International Conference on Information and Communications (IEEE - ICIC), pp 316 - 319 Journals: [J1 ] Nguyen Viet Hung, Nguyen Tien Dzung (2017); A Traffic Monitoring based on Vehicle Density Estimation and Analysis for a Mixed Traffic Flow in a Transport Cross-road, Journal of Science & Technolgoy Technical Universities, No 120, 6/2017, pp 92 - 98 [J2 ] Nguy„n Vi»t H÷ng, Nguy„n Thà Th£o, Ø Huy Khỉi, Nguy„n Ti‚n Dơng (2017); Mỉ H…nh Hâa Ph÷ìng Ph¡p o Tc Tổ Dỹa Trản Xò Lỵ nh, Tp ch‰ Khoa håc v Cỉng ngh», ⁄i håc Th¡i Nguy¶n, ISSN 1859 - 2171, T“p 169 sŁ 09 n«m 2017, pp 39 - 44 126 127 II: C C C˘NG TR NH LI N QUAN TRÜC TI P N LU N N ANGCH˝K TQU PH NBI N [J3 ] Nguyen Viet Hung, Nguyen Tien Dzung (2019); Traffic Density Based Modeling According to Vehicle Sizes T ILI UTHAMKH O [1] Juan Guerrero-Ib¡nez, Sherali Zeadally, and Juan Contreras-Castillo Sensor technologies for intelligent transportation systems In Sensors, volume 18, pages 24, 2018 [2] Vinh Du Mai and Duoqian Miao and Ruizhi Wang Vietnam License Plate Recognition System based on Edge Detection and Neural Networks In Journal of Information and Computing Science, volume 8, pages 27 40, 2013 [3] Tr÷ìng QuŁc B£o Nh“n dng bin s v m s lữổng xe ổtổ trản ÷íng cao tŁc In T⁄p ch‰ Tü ºng hâa ng y nay, volume 189, pages 42 43, 2016 [4] Nguy„n Vôn Côn, Nguyn Tiản Hững, Dữỡng Phú Thun, and Nguyn ông Tin Phữỡng phĂp phƠn loi nhanh phữỡng tiằn giao thổng dỹa trản ữớng vin In K yu Hi ngh Quc gia ln thứ VIII v Nghiản cứu cỡ bÊn v øng dưng Cỉng ngh» thỉng tin (FAIR), pages 581 589, 2015 [5] Viet-Hoa Do, Le-Hoa Nghiem, Ngoc Pham Thi, and Nam Pham Ngoc A simple camera calibration method for vehicle velocity estimation In 12th International Conference on Electrical Engineering /Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pages 5, 2015 [6] QuangTuĐn Nguyn, Anh TuĐn Nguyn, and Vôn Ngồ La Tri”n khai h» thŁng giao thæng thæng minh t⁄i vi»t nam In TÜ ¸NG H´A NG Y NAY, number 162 in 8/2014, 2014 [7] Yilin Zhao Mobile Phone Location Determination and ITS Impact on Intelligent Transportation Systems In IEEE Transactions on Intelligent Transportation Systems, volume 1, pages 55 64, Mar, 2000 [8] Shunsuke Kamijo, Yasuyuki Matsushita, Katsushi Ikeuchi, and Masao Sakauchi Traffic monitoring and accident detection at intersections In IEEE TRANSAC-TIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, volume 1, pages 108 118, 2000 [9] Trista Lin, Herv† Rivano, and Fr†d†ric Le Mouel A survey of smart parking so-lutions In IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, pages 25, 2017 [10] Robert G Keys Cubic convolution interpolation for digital image processing In IEEE Transactions on Acoustics, Speech, and Signal Processing, volume VOL ASSP-29, NO 6, page 1153 1160., DECEMBER 1981, [11] Ming-Tuo Zhou, Yan Zhang, and L T Yan Road Traffic Estimation using Cellular Network Signaling in Intelligent Transportation Systems Nova Science Nova Science Publishers, 2009 [12] Gayathri Chandrasekaran, Tam Vu, Alexander Varshavsky, Marco Gruteser, Richard P Martin, Jie Yang, and Yingying Chen Vehicular Speed Estimation using Received Signal Strength from Mobile Phones In Proceedings of the 12th 128 129 ACM international conference on Ubiquitous computing - Ubicomp ’10, pages 237 240, 2010 [13] Jun Hu, Wei Liu, Huai Yuan, and Hong Zhao A Multi View Vehicle Detection Method Based on Deep Neural Networks In International Conference on Mea-suring Technology and Mechatronics Automation (ICMTMA) 9th, pages 86 89, 2017 [14] Lawrence, A Klein, Milton, K Mills, and David, R P Gibson Traffic Detector Handbook U.S Federal Highway Administration, 1970 [15] C J Pellerin and M H Acuna A Miniature Two-Axis Fluxgate Magnetometer Technical Report D-5325, National Aeronautics and Space Administration (NASA), Washington, D C, 1970 [16] C P Curie Jacques D†veloppement par compression de l’†lectricit† polaire dans les cristaux h†mi–dres faces inclin†es In Bulletin de la Soci†t† min†rologique de France 3, 1880 [17] S A Ahmed, T M Hussain, and T N Saadawi Active and passive infrared sensors for vehicular traffic control In Proceedings of IEEE Vehicular Technology Conference (VTC), volume 2, pages 1393 1397, 1994 [18] Youngtae Jo and Inbum Jung Analysis of Vehicle Detection with WSN Based Ultrasonic Sensors In sensors, pages 14050 14069, 2014 [19] J F Forren and D Jaarsma Traffic monitoring by tire noise In Proceedings of Conference on Intelligent Transportation Systems, pages 177 182, 1997 [20] Wern Yarng Shieh and Ti Ho Wang and Yen Hsih Chou and Chi Chang Huang Design of the Radiation Pattern of Infrared Short Range Communication Systems for Electronic Toll Collection Applications In IEEE Transactions on Intelligent Transportation Systems, volume 9, 2008 [21] Xin Yu, P D Prevedouros, and Goro Sulijoadikusumo Evaluation of autoscope, smartsensor hd, and infra-red traffic logger for vehicle classification In Journal of the Transportation Research Board, pages 77 86, 2010 [22] X Yu, G Sulijoadikusumo, H L Li, and P Prevedouros Reliability of automatic traffic monitoring with non-intrusive sensors In Engineers 11th International Conference of Chinese Transportation Professionals (ICCTP), pages 4157 4169, 2011 [23] Robert P Loce, Raja Bala, and Mohan Trivedi Computer Vision and Imaging in Intelligent Transportation Systems WILEY - IEEE PRESS, 2017 [24] M Shevenell Survey of autonomous imaging In IEEE OCEANS, pages 224 228, 1984 [25] Bin Tian, Qingming Yao, Yuan Gu, Kunfeng Wang, and Ye Li Video processing techniques for traffic flow monitoring: A survey In 14th International IEEE Conference on Intelligent Transportation Systems, pages 1103 1108, 2011 [26] Panos G Michalopoulos Vehicle detection video through image processing: the autoscope system In IEEE Transactions on Vehicular Technology, volume 40, pages 21 29, 1991 [27] George Kopsiaftis and Konstantinos Karantzalos Vehicle detection and traffic density monitoring from very high resolution satellite video data In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pages 1881 1884, 2015 130 [28] Massimo Piccardi Background subtraction techniques: a review In IEEE International Conference on Systems, Man and Cybernetics, pages 3099 3104, 2004 [29] Christof Ridder, Olaf Munkelt, and Harald Kirchner Adaptive background estimation and foreground detection using In Proc ICRAM, pages 193 199, 1995 [30] Chris Stauffer and W E L Grimson Adaptive Background Mixture Models for Real-Time Tracking In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 2, pages 246 252, 1999 [31] R Cucchiara, M Piccardi, and P Mello Image analysis and rule-based reasoning for a traffic monitoring system In IEEE Transactions on Intelligent Transporta-tion Systems, volume 1, pages 119 130, 2000 [32] A J Lipton, H Fujiyoshi, and R S Patil Moving target classification and track-ing from real-time video In Proceedings Fourth IEEE Workshop on Applications of Computer Vision WACV’98, pages 14, 1998 [33] Sok–mi Ren† Emmanuel Datondji, Yohan Dupuis, Peggy Subirats, and Pascal Vasseur A survey of vision-based traffic monitoring of road intersections In IEEE Transactions on Intelligent Transportation Systems, volume 17, pages 2681 2698, 2016 [34] Xueyun Chen, Shiming Xiang, Cheng-Lin Liu, and Chun-Hong Pan Vehicle detection in satellite images by hybrid deep convolutional neural networks In IEEE Geoscience and Remote Sensing Letters, pages 1797 1801, 2014 [35] Zhaojin Zhang, Cunlu Xu, and Wei Feng Road vehicle detection and classification based on deep neural network In IEEE International Conference on Software Engineering and Service Science (ICSESS) 7th, pages 675 678, 2016 [36] Yanjun Liu, Na Liu, Hong Huo, and Tao Fang Vehicle detection in high resolution satellite images with joint-layer deep convolutional neural networks In Interna-tional Conference on Mechatronics and Machine Vision in Practice (M2VIP) 23rd, pages 6, 2016 [37] Shoaib Aza, Aasim Rafique, and Moongu Jeon Vehicle pose detection using region based convolutional neural network In International Conference on Control, Automation and Information Sciences (ICCAIS), pages 194 198, 2016 [38] Rindra Wiska, Machmud R Alhamidi, Novian Habibie, Ari Wibisono, Petrus Mursanto, Doni H Ramdhan, M Febrian Rachmadi, and Wisnu Jatmiko Vehi-cle traffic monitoring using single camera and embedded systems In 2016 In-ternational Conference on Advanced Computer Science and Information Systems (ICACSIS), pages 117 121, 2016 [39] Chen Wei-Gang and Xu Bin Detecting moving shadows in video sequences using region level evaluation for vision-based vehicle detection In 2010 Fifth International Conference on Frontier of Computer Science and Technology, pages 142 146, 2010 [40] Yu Yang, Yu Ming, and Ma Yongchao A strategy to detect the moving vehicle shadows based on gray-scale information In 2009 Second International Conference on Intelligent Networks and Intelligent Systems, pages 358 361, 2009 [41] Nur Shazwani A., M M Ibrahim, N M Ali, and Nur Fatin Izzati Y Vehicle detection based on underneath vehicle shadow using edge features In 2016 6th IEEE International Conference on Control System, Computing and Engineering, pages 407 412, 25 27 November 2016, Penang, Malaysia, 2016 131 [42] Asmita Jondhale, Gautami Das, and Samadhan Sonavane Ocr and rfid enabled vehicle identification and parking allocation system In 2015 International Con-ference on Pervasive Computing (ICPC), pages 4, 2015 [43] Judith Sen E, Deepa Merlin Dixon K, Ansy Anto, Anumary M V, Daine Micheal, Fincy Jose, and Jinesh K J Advanced license plate recognition system for car parking In International Conference on Embedded Systems (ICES 2014), pages 162 165, 2014 [44] Gautam B Singh and Haiping Song Comparison of hidden markov models and suppor vector machines for vehicle crash detection In 2010 International Conference on Methods and Models in Computer Science (ICM2CS-2010), pages 6, 2010 [45] Jooyoung Lee and Kitae Jang Proactive detection of crash hotspots using invehicle driving recorder In 2016 3rd Asia-Pacific World Congress on Computer Science and Engineering, pages 193 198, 2016 [46] Yifu Liu, Paul Watta, Bochen Jia, and Yi Lu Murphey Vehicle position and con-text detection using v2v communication with application to pre-crash detection and warning In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pages 7, 2016 [47] Shi, X and Zhao, W and Shen, Y Automatic license plate recognition system based on color image processing In Proceedings of the International Conference on Computational Science and Its Applications (ICCSA 2005), pages 1159 1168, 2005 [48] Lee, E R and Kim, P K and Kim, H Automatic recognition of a car license plate using color image processing In IEEE International Conference on Image Processing, volume 2, pages 301 305, 1994 [49] Zheng, D and Zhao, Y and Wang, J An efficient method of license plate location In Pattern Recognition Letters, pages 2431 2438, 2005 [50] Bai, H and Liu, C A hybrid license plate extraction method based on edge statis-tics and morphology In 17th International Conference on Pattern Recognition, pages 831 834, 2004 [51] Viola, P and Joes, M Rapid object detection using a boosted cascade of simple features In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001 [52] Jung, K., Kim, K I., and Jain, A K Text information extraction in images and video: A survey In Pattern Recognition, 2004 [53] Casey, R G and Lecolinet, E A survey of methods and strategies in character segmentation In IEEE Transactions on Pattern Analysis and Machine Intelli-gence, pages 690 706, 1996 [54] Sumathi, C P., Santhanam, T., and Gayathri, G A survey on various approaches of text extraction in images In International Journal of Computer Science and Engineering Survey, pages 27 42, 2012 [55] Anagnostopoulos, C N E and Anagnostopoulos, I E and Psoroulas, I D and Loumos, V and Kayafas, E License plate recognition from still images and video sequences: A survey In IEEE Transactions on Intelligent Transportation Systems, pages 377 391, 2008 132 [56] Du, S and Ibrahim, M and Shehata, M and Badawy, W Automatic License Plate Recognition (ALPR) In IEEE Transactions on Circuits and Systems for Video Technology, pages 311 325, 2013 [57] Apiwat Sangnoree Vehicular separation by thermal features relative angle for nighttime traffic In 6th International Conference on New Trends in Information Science, Service Science and Data Mining (ISSDM2012), pages 796 773, 2012 [58] Yoichiro Iwasaki, Masato Misumi, and Toshiyuki Nakamiya Robust Vehicle De-tection under Various Environments to Realize Road Traffic Flow Surveillance Using an Infrared Thermal Camera In The Scientific World Journal, pages 11, 2015 [59] Mark W Koch and Kevin T Malone A Sequential Vehicle Classifier for Infrared Video using Multinomial Pattern Matching In Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06), 2006 [60] K M Simonson Multinomial Pattern Matching: A Robust Algorithm for Target Identification In Proceedings of Automatic Target Recognizer Working Group (ATRGW), 1997 [61] A Wald Sequential Analysis In New York City: Courier Corporation, 1973 [62] R O Duda, P E Hart, and D G Stork Pattern Classification In New York: John Wiley & Sons, Inc., 2012 [63] Z Chen, T Ellis, and S Velastin Vehicle Type Categorization: A Compari-son of Classification Schemes In International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 74 79, 2011 [64] X Ma, W Eric, and L Grimson Edge based rich representation for vehicle classification In IEEE International Conference on Computer Vision, volume 2, 2005 [65] Z Chen, T Ellis, and S Velastin Vehicle Detection, Tracking and Classification in Urban Traffic In Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 951 956, 2012 [66] S Gupte and et al Detection and Classification of Vehicles In IEEE Transactions on Intelligent Transportation Systems, pages 37 47, 2002 [67] B Morris and M Trivedi Improved Vehicle Classification in Long Traffic Video By Cooperating Tracker And Classifier Modules In Proceedings of the IEEE International Conference on Video and Signal Based Surveillance, 2006 [68] Chung Lin Huang and Wen Chieh Liao A Vision Based Vehicle Identification System In Proceedings of the IEEE 17th International Conference on Pattern Recognition, 2004 [69] M Kafai and B Bhanu Dynamic Bayesian Networks for Vehicle Classification In Video In IEEE Transactions on Industrial Informatics, pages 100 109, 2012 [70] Y Shan, H S Sawhney, and R Kumar Unsupervised learning of discriminative edge measures for vehicle matching between nonoverlapping cameras In IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 700 711, 2008 [71] O Hasegawa and T Kanade Type Classification, Color Estimation, and Specific Target Detection of Moving Targets On Public Streets In Machine Vision and Applications, pages 116 121, 2005 133 [72] H C Karaimer, I Cinaroglu, and Y Bastanlar Combining Shape Based and Gra-dient Based Classifiers For Vehicle Classification In Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC), pages 800 805, 2015 [73] S Sen Ching and S Cheung and C Kamath Robust techniques for background subtraction in urban traffic video In Video Communications and Image Process-ing, pages 881 892, 2004 [74] H Sakoe and S Chiba Dynamic programming algorithm optimization for spo-ken word recognition In IEEE Transactions on Acoustics, Speech and Signal Processing, pages 43 49, 1978 [75] D P Huttenlocher, G Klanderman, and W J Rucklidge Comparing images using the Hausdorff distance In IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 850 863, 1993 [76] A Thayananthan and et al Shape context and chamfer matching in cluttered scenes In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, pages 7, 2003 [77] D G Lowe Distinctive image features from scale invariant keypoints In International Journal of Computer Vision, volume 60(2), pages 91 110., 2004 [78] R Fergus, P Perona, and A Zisserman Object class recognition by unsupervised scale invariant learning In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 2, 2003 [79] D Dalal and B Triggs Histograms of oriented gradients for human detection In IEEE Computer Society Conference on Computer Vision and Pattern Recog-nition, volume 1, 2005 [80] T Gandhi and M M Trivedi Video based surround vehicle detection, classification and logging from moving platforms: Issues and approaches In IEEE Intelligent Vehicles Symposium, 2007 [81] X Cao and et al Linear SVM classification using boosting HOG features for ve-hicle detection in low altitude airborne videos In IEEE International Conference on Image Processing (ICIP 2011), 2011 [82] F Han and et al A two stage approach to people and vehicle detection with HOG based SVM In Performance Metrics for Intelligent Systems 2006 Workshop, 2006 [83] S Tan and et al Inverse perspective mapping and optic flow: A calibration method and a quantitative analysis In Image and Vision Computing, volume 24(2), pages 153 165, 2006 [84] H A Mallot and et al Inverse perspective mapping simplifies optical flow com-putation and obstacle detection In Biological Cybernetics, volume 64(3), pages 177 185, 1991 [85] T Lindeberg Scale space theory: A framework for handling image structures at multiple scales In Proceedings of the CERN School of Computing, 1996 [86] Z Chen and et al Road vehicle classification using support vector machines In Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems, volume 4, 2009 134 [87] A Gaszczak, T P Breckon, and J Han Real time People and Vehicle Detection from UAV Imagery In Proceedings of IST SPIE Electronic Imaging, San Francisco, CA, USA,, 2011 [88] G Y Song, K Y Lee, and J W Lee Vehicle Detection by Edge Based Candidate Generation And Appearance Based Classification In IEEE Intelligent Vehicles Symposium, pages 428 433, 2008 [89] Hongliang Bai, Jianping Wu, and Changpin Liu Motion and Haar-like Features Based Vehicle Detection In 2006 12th International Multi-Media Modelling Con-ference, pages 356 359, 2006 [90] A Haselhoff and A Kummert A vehicle detection system based on Haar and triangle features In IEEE Intelligent Vehicles Symposium, 2009 [91] H Abdi and L J Williams Principal component analysis In Wiley Interdisciplinary Reviews: Computational Statistics, pages 433 459, 2010 [92] C Zhang, X Chen, and W B Chen A PCA Based Vehicle Classification Frame-work In Proceedings of the 22nd International Conference on Data Engineering Workshops, 2006 [93] J W Wu and X Zhang A PCA classifier and its application in vehicle detection In Proceedings of the International Joint Conference on Neural Networks (IJCNN’01), volume 1, 2001 [94] E Kreyszig Advanced Engineering Mathematics New York: John Wiley & Sons, 1988 [95] M Turk and A P Pentland Face recognition using eigenfaces In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 91), 1991 [96] J Laws, N Bauernfeind, and Y Cai Feature hiding in 3D human body scans In Information Visualization, pages 271 278, 2006 [97] A H S Lai, G Fung, and N Yung Vehicle type classification from visual based dimension estimation In Proceedings of the 2001 IEEE Intelligent Transportation Systems, 2001 [98] Wilhelm Leutzbach Introduction to the Theory of Traffic Flow Springer Verlag, 1988 [99] John Canny A computational approach to edge detection In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume PAMI-8, NO 6, page 679 698, Nov 1986 [100] Sekar V, V Duraisamy, and Remimol A M An approach of image scaling using dwt and bicubic interpolation In Green Computing Communication and Electrical Engineering (ICGCCEE), pages 5, 2014 [101] Zhou Dengwen An edge-directed bicubic interpolation algorithm In 3rd International Congress on Image and Signal Processing (CISP2010), page 1186 1189, 2010 [102] Shilpa, Prathap H.L, and Sunitha M.R A survey on moving object detection and tracking techniques In International Journal Of Engineering And Computer Science, volume 5, pages 16376 16382, 2016 135 [103] Payal Panchal, Gaurav Prajapati, Savan Patel, Hinal Shah4, and Jitendra Nasri-wala A review on object detection and tracking methods In INTERNATIONAL JOURNAL FOR RESEARCH IN EMERGING SCIENCE AND TECHNOL-OGY, volume 2, pages 12, 2015 [104] S R Balaji and S Karthikeyan A survey on moving object tracking using image processing In International Conference on Intelligent Systems and Control (ISCO), pages 469 474, 2017 [105] Bruce D Lucas and Takeo Kanade An iterative image registration technique with an application to stereo vision In Proceedings of Imaging Understanding Workshop, pages 121 130, 1981 [106] Charles Anum Adams, Mohammed Abdul Muhsin Zambang, and Richter Opoku Boahen Effects of motorcycles on saturation flow rates of mixed traffic at signal-ized intersections in ghana In International Journal of Traffic and Transportation Engineering, pages 94 101, 2015 [107] Saowaluck Kaewkamnerd, Jatuporn Chinrungrueng, Ronachai Pongthornseri, and Songphon Dumnin Vehicle classification based on magnetic sensor signal In IEEE International Conference on Information and Automation, pages 935 939, 2010 [108] Jun-Wei Hsieh, Shih-Hao Yu, Yung-Sheng Chen, and Wen-Fong Hu Automatic traffic surveillance system for vehicle tracking and classification In IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, vol-ume 7, pages 175 187, 2006 [109] YuQiang Liu and Kunfeng Wang Vehicle classification system based on dynamic bayesian network In Service Operations and Logistics, and Informatics (SOLI), pages 22 26, 2014 [110] Prateek, G.V, K Nijil, and K.V.S Hari Classification of vehicles using magnetic field angle model In Intelligent Systems Modelling & Simulation (ISMS), pages 214 219, 2013 [111] Hakki Can Karaimer, Ibrahim Cinaroglu, and Yalin Bastanlar Combining shape-based and gradient-based classifiers for vehicle classification In 18th IEEE Inter-national Conference on Intelligent Transportation Systems, Spain, pages 800 805, 2015 [112] D F Llorca, C Salinas, M Jim†nez, I Parra, A G Morcillo, R Izquierdo, J Lorenzo, and M A Sotelo Two-camera based accurate vehicle speed measurement using average speed at a fixed point In IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pages 2533 2538, 2016 [113] Chen Yajun, Zhang Erhu, and Kang Xiaobing Divisional velocity measurement for high-speed cotton flow based on double ccd camera and image cross-correlation algorithm In The 11th IEEE International Conference on Electronic Measurement & Instruments, pages 202 206, 2013 [114] Fumiaki Mitsugi, Toshiyuki Nakamiya, Yoshito Sonoda, and Hiroharu Kawasaki High-speed camera and fibered optical wave microphone measurements on surface-dielectric-barrier discharges In IEEE Transactions on Plasma Science, pages 2642 2648, 2015 [115] Diogo Carbonera Luvizon, Bogdan Tomoyuki Nassu, and Rodrigo Minetto A video-based system for vehicle speed measurement in urban roadways In IEEE Transactions On Intelligent Transportation Systems, pages 12, 2016 136 [116] Ronald E Crochiere and Lawrence R Rabiner Multirate Digital Signal Process-ing Prentice-Hall, Inc., Englewood Cliffs, 1983 [117] William S Russell Polynomial interpolation schemes for internal derivative dis-tribution on structured grids In Applied Numberical Mathematics 17, page 129 171, 1995 [118] Erik Meijering A chronology of interpolation: from ancient astronomy to modern signal and image processing In Proceedings of the IEEE, volume 90 Issue 3, pages 319 342, Mar 2002 [119] David A Forsyth and Jean Ponce Computer Vision A Modern Approach Pear-son; edition, second edition edition, 2012 [120] Lishao Wang, Baohua Mao, Shaokuan Chen, and Kuiling Zhang Mixed flow simulation at urban intersections: Computational comparisons between conflictpoint detection and cellular automata models In International Joint Conference on Computational Sciences and Optimization, pages 100 104, 2009 [121] Mianfang Liu, Shengwu Xiong, Xiaohan Yu, Pengfeng Duan, and Jun Wang Behavior characteristics of mixed traffic flow on campu In Computational Intelligence in Vehicles and Transportation Systems (CIVTS), pages 140 147, 2014 [122] Mohamed A Khamis and Walid Gomaa Enhanced multiagent multi-objective reinforcement learning for urban traffic light control In International Conference on Machine Learning and Applications 11th, pages 586 591, 2012 [123] Maram Bani Younes and Azzedine Boukerche Intelligent traffic light controlling algorithms using vehicular networks In IEEE TRANSACTIONS ON VEHICU-LAR TECHNOLOGY, pages 13, 2015 [124] Massimo Magrini, Davide Moroni, Giovanni Palazzese, Gabriele Pieri, Giuseppe Riccardo Leone, and Ovidio Salvetti Computer vision on embedded sensors for traffic flow monitoring In IEEE 18th International Conference on Intelligent Transportation Systems, Spain, pages 161 166, 2015 [125] Girija H Kulkarni and Poorva G Waingankar Fuzzy logic based traffic light con-troller In 2007 International Conference on Industrial and Information Systems, pages 107 110, 2007 [126] Suhail M Odeh Hybrid algorithm: fuzzy logic-genetic algorithm on traffic light intelligent system In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pages 7, 2015 [127] Pallavi Choudekar, Sayanti Banerjee, and M.K.Muju Implementation of image processing in real time traffic light control In Electronics Computer Technology (ICECT), 2011 3rd International Conference on, pages 94 98, 2011 [128] Nobuyuki Otsu A threshold selection method from gray-level histograms In IEEE Transactions on Systems, Man, and Cybernetics, pages 62 66, 1979 [129] Transportation Research Board Highway capacity manual In National Research Council, 2000 [130] B John Glen Wardrop Some Theoretical Aspectsof Road Traffic Research In ROAD ENGINEERING DIVISION MEETING, pages 325 362, 1952 [131] Lighthill, M J and et al A Theory of Traffic Flow on Long Crowded Roads Number 317-345 1955 137 [132] M Treiber and A Kesting Traffic Flow Dynamics: Data, Models and Simulation Springer, 2013 [133] F van Wageningen Kessels Multi class cotinuum traffic flow models: Analysis and simulation methods Ph.D Dessertation, Delft University of Technology, 2013 [134] I K Sarosh and M Pawan Modeling Heterogeneous Traffic Flow In Transportation Research Record, volume 1678, pages 234 241, 1999 [135] Sai Kiran and A Verma Review of Studies on Mixed Traffic Flow: Perspective of Developing Economies In Transp in Dev Econ, volume 2:5 of Spinger, pages 16, 2016 [136] M G Sosina Modeling heterogeneous vehicular traffic for intelligent transport system applications In Universit† Cæte d’Azur, 2018 [137] Y Kim and F L Hall Relationships Between Occupancy and Density Reflect-ing Average Vehicle Lengths In Transportation Research Record: Journal of the Transportation Research Board, volume 1883, pages 85 93, 2004 [138] Thamizh, A V and Reebu, Z K Methodology for Modeling Highly Heterogeneous Traffic Flow In Journal of Transportation Engineering ASCE, pages 544 551, 2005 [139] Thamizh, A V and Dhivya, G Measuring Heterogeneous Traffic Density In International Sholarly and Scientific Research & Innovation 2, volume 10, pages 236 240, 2008 [140] Thamizh, A V and Dhivya, G Measurement of Occupancy of Hetegeneous Traffic using Simulation Technique In Proceedings of the 12th IFAC Symposium on Transportation Systems Redondo Beach, pages 19 24, 2009 [141] Lasmini, A and et al Empirical Analysis of Heterogeneous Traffic Flow In Proceedings of the Eastern Asia Society for Transportation Studies, 2013 [142] M Ranju and R Gitakrishnan Heterogeneous traffic flow modelling using macro-scopic continuum model In Procedia - Social and Behavioral Sciences, volume 104, pages 402 411, 2013 [143] M Ranju and R Gitakrishnan Heterogeneous traffic flow modelling using second-order macroscopic continuum model In Physics Letters A, volume 381, pages 115 123, 2017 [144] A Samuel, E St†phane, and B Samir Naturalistic study of riders’ behaviour in lane splitting situations In Verlag London: Springer, 2014 [145] K Anurag, S Ayush, and S Chetan Smart Traffic Lights Switching and Traffic Density Calculation using Video Processing In Proceedings of 2014 RAECS UIET Panjab University Chandigarh, 2014 [146] Khushi Smart Control of Traffic Light System using Image Processing In International Conference on Current Trends in Computer, Electrical, 2017 [147] S M Shinde Adaptive traffic light control system In 1st International Conference on Intelligent Systems and Information Management (ICISIM), 2017 [148] R R Jegan, E Sree Devi, M Sindhuja, S Pushna, and D Sudhaa Traffic Light Controller Using Sound Sensors and Density Sensors In Proceedings of the 2nd International Conference on Inventive Communication and Computational Technologies (ICICCT 2018), 2018 138 [149] E P Uthara, T Athira, K T Vishnupriya, and A B Arun Density Based Traffic Control System Using Image Processing In Proceedings of 2018 Inter-national Conference on Emerging Trends and Innovations in Engineering and Technological Research (ICETIETR), 2018 [150] D Aman, Akshdeep, and R Sagar Implementation of an Intelligent Traffic Control System and Real Time Traffic Statistics Broadcasting In International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2017 [151] A o Wahban, A Issam, and B Mohamed Real Time Traffic Light Control System Based on Background Updating and Edge Detection In International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), 2019 [152] T Taqi and H Eklas Density Based Smart Traffic Control System Using Canny Edge Detection Algorithm for Congregating Traffic Information In 3rd Inter-national Conference on Electrical Information and Communication Technology (EICT), 2017 [153] L M Fred and S W Scott Principles of Highway Engineering and Traffic Analysis (5th) In USA: Wiley, 2013 [154] J Kankesu Advanced Technologies In Croatia: In-Teh, 2009 [155] B Greenshields The Density Factor in Traffic Flow In Traffic Engineering, volume 30, pages 26 28, 1960 [156] P Athol Interdependence of Certain Operational Charcteristics within a Moving Traffic Stream In Highway Research Recoed, number Natinal Research Council, Washington, pages 58 87, 1965 [157] J G Nicholas and A H Lester Traffic & Highway Engineering In Toronto, Canada: Cengage Learning, 2009 PHƯ LƯC A B£ng g.1: B£ng ph¥n lo⁄i ph÷ìng ti»n theo trưc b¡nh xe STT Nhâm ph÷ìng ti»n Chó th‰ch SŁ trưc b¡nh xe Xe g›n m¡y Xe g›n m¡y 2 Xe ỉ-tỉ chð kh¡ch T§t c£ ỉ-tỉ chð ng÷íi 2, ho°c Xe b¡n t£i, xe chð ng÷íi lo⁄i Xe hai trưc v môc nhä câ ho°c khỉng k†o theo môc mºt trưc ho°c hai tröc 2, ho°c 4 Xe kh¡ch Xe kh¡ch ho°c Xe t£i hai tröc, s¡u b¡nh Xe t£i hai tröc Xe t£i ba tröc Xe t£i ba tröc, xe k†o ba tröc khỉng câ môc Xe t£i bŁn trưc trð lản Xe tÊi bn, nôm, sĂu, bÊy trửc tr l¶n Xe t£i bŁn trưc trð xuŁng Xe t£i, xe k†o hai tröc k†o mºt ho°c ho°c hai môc Xe k†o hai trưc k†o môc trưc, Xe t£i n«m trưc xe k†o ba trưc k†o môc trưc, xe t£i ba trưc k†o môc hai trưc 10 Xe hìn s¡u trưc K‚t hỉp tr lản 11 Xe nôm trửc tr xung Kt hổp ho°c 12 Xe t£i s¡u trưc K‚t hỉp 13 Xe t£i b£y trưc trð l¶n K‚t hỉp trð l¶n 139 ... giao thỉng ổ th c biằt l H Ni v TP Hỗ Ch Minh l i tữổng nghiản cứu ca lun Ăn B i toĂn quÊn lỵ giao thổng ữổc chia nhọ tợi tng on ữớng v tng nút giao thổng C¡c nót giao thỉng ln l tr‰ chuy”n giao. .. ¢ l›p °t th¶m c¡c camera gi¡m s¡t c¡c nót giao thỉng quan trång l m cì sð tham chi‚u cho c¡c ho⁄t ng xò lỵ vi phm v giĂm sĂt thng kả l÷ỉng ph÷ìng ti»n giao thỉng di chuy”n qua c¡c nót n y Ơy... lữu l÷ỉng ph÷ìng ti»n giao thỉng qua h» thŁng camera gi¡m s¡t b H» thŁng Giao thæng Thæng minh Tł c¡c dÔn chứng, cĂc vôn bÊn ca chnh ph v s liằu trản b i toĂn quÊn lỵ giao thổng tr nản cĐp bĂch