期刊文献+

基于车牌照模型的大地坐标系下车辆精确定位 被引量:2

Precise vehicle locating in geodetic coordinates based on the vehicle license plate model
原文传递
导出
摘要 为了对车辆位置进行快速准确定位,提出了一种基于车牌照模型来获取大地坐标系下车辆精确位置的方法。首先通过离线标定的摄像机参数,推导图像平面与道路平面之间的坐标转换关系;然后对感兴趣区域内的车辆进行身份识别,根据车牌照宽度和高度已知,以及车牌照平面与道路平面垂直的约束条件,建立车牌照模型来对车辆进行精确定位;最后引入东北天坐标系来实现车辆定位结果从图像坐标系到大地坐标系的转换。实验结果表明该方法能对车辆位置进行快速精确定位。 This paper presents a quick and accurate method to identify a vehicle position in a geodetic coordinate system based on a vehicle license plate model.The camera parameters are calibrated off-line to develop a coordinate transformation between the image plane and the road plane.Then,the vehicle is identified within the region of interest and the vehicle license plate model is used to precisely position the vehicle,since the vehicle license plate's width and height are known and the vertical distance between vehicle license plate plane and the road plane is constrained.Finally,an east-north-top coordinate is used to convert the vehicle positioning results from the image coordinates to the geodetic coordinates.Tests show that this method can quickly and accurately identify the vehicle position.
出处 《清华大学学报(自然科学版)》 EI CAS CSCD 北大核心 2014年第12期1566-1572,共7页 Journal of Tsinghua University(Science and Technology)
基金 国家自然科学基金面上项目(60872085 61174068) 北京工业职业技术学院科研项目(bgzyky201407)
关键词 车辆定位 车牌照模型 大地坐标系 东北天坐标系 vehicle location vehicle license plate model geodetic coordinate east-north-top coordinates
  • 相关文献

参考文献13

  • 1Du S,Ibrahim M,Shehata M,et al.Automatic license plate recognition(ALPR):A state-of-the-art review[J].IEEE Transactions on Circuits and Systems for Video Technology,2013,23(2):311-325.
  • 2Hofleitner A,Herring R,Bayen A.Arterial travel time forecast with streaming data:A hybrid approach of flow modeling and machine learning[J].Transportation Research Part B,2012,46:1097-1122.
  • 3王龙飞.基于车牌照的车辆出行轨迹分析方法与实践研究[D].西安:长安大学,2012.
  • 4Buch N,Velastin S A,Orwell J.A review of computer vision techniques for the analysis of urban traffic[J].IEEE Transactions on Intelligent Transportation Systems,2011,12(3):920-939.
  • 5Ghosh N,Bhanu B.Incremental unsupervised threedimensional vehicle model learning from video[J].IEEE Transactions on Intelligent Transportation Systems,2010,11(2):423-440.
  • 6曹洁,王伟.基于立方体模型和EKF的运动汽车跟踪算法研究[J].计算机工程与应用,2010,46(22):236-238. 被引量:2
  • 7Peng Y,Xu M,Ni Z,et al.Combining front vehicle detection with 3D pose estimation for a better driver assistance[J].International Journal of Advanced Robotic Systems,2012,93(9):1-15.
  • 8Alefs B,Schreiber D.Accurate speed measurement from vehicle trajectories using AdaBoost detection and robust template tracking[C]//Proceedings of IEEE Intelligent Transportation Systems Conference.Seattle,WA,USA:IEEE Press,2007:405-412.
  • 9陈阳舟,刘星,辛乐,杨德亮.基于Co-training方法的车辆鲁棒检测算法[J].北京工业大学学报,2013,39(3):394-401. 被引量:1
  • 10Kamal A T,Farrell J A,Roy-Chowdhury A K.Information weighted consensus filters and their application in distributed camera networks[J].IEEE Transactions on Automatic Control,2013,58(12):3112-3125.

二级参考文献19

  • 1李金宗,朱兵,魏祥泉.图像序列的准三维运动目标自寻的跟踪算法[J].系统工程与电子技术,2006,28(4):568-572. 被引量:1
  • 2侯志强,韩崇昭.视觉跟踪技术综述[J].自动化学报,2006,32(4):603-617. 被引量:254
  • 3Kyrki V, Kragic D.Integration of model-based and model-free cues for visual object tracking in 3D[C]//Proceedings of the 2005 IEEE International Conference on Robotics and Automation Barcelona, Spain,2005,4:1554-1560.
  • 4Sidorow K, Hicks Y, Marshall D, et al.Real-time multi camera 3D tracking system[C]//3rd European Conference on CVMP, 2006:191-211.
  • 5潘平俊,冯新喜,刘英坤.一种修正的自适应常加速模型[J].电光与控制,2007,14(5):163-167. 被引量:1
  • 6CHANG Wen-chung, CHO Chih-wei. Online boosting for vehicle detection [ J ]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2010, 40 (3) : 892-902.
  • 7WANG Wei-hong, SHEN Chun-hua, ZHANG Jian, et al. A two-layer night-time vehicle detector [ C ]//Proceedings of the 2009 Digital Image Computing: Techniques and Applications. Piscataway: IEEE Computer Society, 2009 : 162-167.
  • 8WU Chun-peng, DUAN Li-juan, MIAO Jun, et al. Detection of front-view vehicle with occlusions using AdaBoost [ C ]// Proceedings 2009 International Conference on Information Engineering and Computer Science. Piscataway: IEEE Computer Society, 2009 : 1-4.
  • 9CHENG Hong, ZHENG Nan-ning, SUN Chong. Boosted Gabor features applied to vehicle detection [ C ] // Proceedings of the 18th International Conference on Pattern Recognition. Piscataway: Institute of Electrical and Electronics Engineers Inc. , 2006: 662-666.
  • 10KONG Fan-jing, YE Qi-xiang, ZHANG Ning, et al. On- road vehicle detection using histograms of multi-scale orientations [ C ] // Proceedings 2009 IEEE Youth Conference on Information, Computing and Telecommunication. Piscataway: IEEE Computer Society, 2009 : 212-215.

共引文献7

同被引文献12

  • 1Nourani-Vatani N, Borges P V K, Roberts J M, et al. On the use of optical flow for scene change detection and description [J]. Journal of Intelligent &Robotic Systems, 2014, 74+ ( 3 ) : 817-846.
  • 2Chen Z, Ellis T. Self-adaptive Gaussian mixture model for ur- ban traffic monitoring system [C] //IEEE International Con- ference on Computer Vision Workshops, 2011: 1769-1776.
  • 3Mejia-Iigo R, Barilla-P6rez M E, Montes-Venegas H A. Co- lor-based texture image segmentation for vehicle detection [C] //6th International Conference on Electrical Engineering, Computing Science and Automatic Control. IEEE, 2009: 1-6.
  • 4Lin B F, Chan Y M, Fu L C, et al. Integrating appearance and edge features for sedan vehicle detection in the blind-spot area [J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13 (2): 737-747.
  • 5Lian J, Zhao C, Zhang B, et al. Vehicle detection based on information fusion of vehicle symmetrical contour and license plate position [J]. Journal of Southeast University, 2012, 28 (2): 240-244.
  • 6Adankon M M, Cheriet M. Support vector machine [M]. Encyclopedia of Biometrics. Springer US, 2009: 1303-1308.
  • 7杨丹,余孟泽.车辆视频检测及阴影去除[J].计算机工程与设计,2011,32(6):2072-2074. 被引量:12
  • 8慕永云,王荣本,赵一兵,郭烈.基于多特征融合的前方车辆检测方法研究[J].计算机应用研究,2011,28(9):3572-3575. 被引量:12
  • 9陈志猛,刘东权.基于对称性的快速车辆检测方法[J].计算机工程与设计,2012,33(3):1042-1046. 被引量:9
  • 10王振亚,曾黄麟.一种基于帧间差分和光流技术结合的运动车辆检测和跟踪新算法[J].计算机应用与软件,2012,29(5):117-120. 被引量:35

引证文献2

二级引证文献10

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部