利用车牌面积进行车距测量的研究

doi:10.16182/j.issn1004731x.joss.201711031

摘要/Abstract

摘要： 为了保证行车安全,预测两车之间的距离是非常重要,对基于视觉的车辆检测及车距测量进行了研究,提出了一种根据车牌在图像中像素面积的大小来计算车距的方法。基于帧间差分与AdaBoost相结合进行车辆检测,并对获取的车尾图像进行处理以及车牌校正及定位;建立了一个车距测量模型,该模型的思想是寻找相机与图像坐标系之间的关系,从而推出本文的车距模型;基于该模型求取车牌在图像中的像素面积,并完成了车距测量试验。试验结果表明,提出车距测量方法将误差控制在7%以内,具备了较高的检测精度,且对车辆距离较远的情况下同样适用,因此本文的车距测量方法能够有效地应用于车辆安全辅助驾驶系统中。

关键词: 帧间差分, Adaboost算法, 车辆检测, 车距测量

Abstract: Estimating the distance between two vehicles is very important for transport safety. In order to keep the driving safety and avoid the traffic accidents, the visual-based vehicle detection and vehicle distance measurement are studied. In order to improve the detection precision and shorten the testing time, frame difference is combined with adaboost algorithm to detect vehicle. In the part of distance measurement, a method to calculate the distance between vehicles based on the pixels size of the vehicle's plate area in the image is proposed. Firstly, the image of the rear end of the vehicle is processed for edge detections and vehicle license plate location and rectification; A vehicle distance measurement model is established, the idea of which is to find the relationship between camera and image coordinate system, so as to deduce the vehicle distance model proposed in this paper. Finally, the pixel area of the license plate in the image is obtained based on the model and the vehicle distance measurement experiment is completed. The experiment results demonstrate that the relative error of vehicle distance measurement is less than 7% and the precision can meet the application for vehicle's safety assistance driving system.

Key words: Inter-frame difference, Adaboost algorithm, Vehicle detection, Vehicle distance measurement

中图分类号:

姚春莲, 冯胜男, 张芳芳, 司慧琳. 利用车牌面积进行车距测量的研究[J]. 系统仿真学报, 2017, 29(11): 2820-2827.

Yao Chunlian, Feng Shengnan, Zhang Fangfang, Si Huilin. Vehicle-Distance Measurement Based on Plate Area[J]. Journal of System Simulation, 2017, 29(11): 2820-2827.

参考文献

[1] Kasuga T, Yakubo S.Design of a dependable model vehicle for rear-end collision avoidance and its evaluation[C]. Instrumentation and Measurement Technology Conference (IMTC), Austin, USA (S1363-1027), 2010: 641-646.
[2] 刘君, 伍璨, 吴思聪. 基于FPGA的多通道超声波测距系统设计[J]. 计算机测量与控制. 2016, 24(1): 78-80.
Liu Jun, Wu Can, Wu Sicong.Design of Multi-channel Ultrasonic Ranging System Based on FPGA[J].Computer measurement and control. 2016, 24(1): 78-80.
[3] 田文成, 周西峰, 郭前岗. 一种改进的超声波测距方法研究[J]. 微型机与应用. 2016, 35(2): 70-72.
Tian Chengwen, Zhou xifeng, Guo qiangang. An improved method study on the ultrasonic distance measurement[J].Micro-computer and application. 2016, 35(2): 70-72.
[4] 于海存, 王官. 基于超声测距的智能车辆防追尾安全测控仪[J]. 仪表技术与传感器, 2013, 6: 37-39.
Yu haicun, Wang gong. Design of Measure and Controller of Intelligent Vehicle Based on Ultrasonic Wave Distance Measuring[J]. Instrument Technique and Sensor, 2013, 6: 37-39.
[5] Tsuchiya J, Hayashi M, Kohno R.Platoon characteristics of automatic vehicle speed control for vehicles equipped with DS-UWB radar[C]//IEEE 11 the International Symposium on Spread Spectrum Techniques and Applications. Taichung, Taiwan, China (S2410-4127), 2010: 125-130.
[6] 靳璐, 付梦印, 王美玲, 等. 基于视觉和毫米波雷达的车辆检测[J]. 红外与毫米波学报, 2015, 33(5): 465-471.
Jin lu, Fu mengyin, Wang meiling, et al. Vehicle detection based on millimeter-wave radar and image fusion techniques[J]. Journal of Infrared and Millimeter Waves, 2015, 33(5): 465-471.
[7] Chen Yandong, Meng Zhiqiang, Liu Jinglin, et al.High-precision infrared pulse laser ranging for active vehicle anti-collision application[C]// 2011 International Conference on Electric Information and Control Engineering. Wuhan, China (S2231-0088), 2011: 1404-1407.
[8] 尹小琴, 朱喜行, 张盼盼. 高速公路车辆防追尾碰撞系统的设计与控制研究[J]. 汽车工程. 2014, 35(9): 1080-1084.
Yin xiaoqin, Zhu xihang, Zhang panpan. Study on the design and control of vehicle rear-end collision avoidance system in freeway[J]. automotive engineering. 2014, 35(9): 1080-1084.
[9] Cai Yingfeng, Wang Hai, Wang Hai, et al.Deep representation and stereo vision based vehicle detection[C]//The 5th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, Shenyang, China (S0976-3252), 2015: 305-310.
[10] Donguk Seo, Hansung Park, Kanghyun Jo, et al.Omnidirectional stereo vision based vehicle detection and distance measurement for driver assistance system[J]. Industrial Electronics Society, IECON 2013-39th Annual Conference of the IEEE (S2410-0439), 2013: 5507-5511.
[11] Mahdi Rezaei, Mutsuhiro Terauchi, Reinhard Klette.Robust Vehicle Detection and Distance Estimation Under Challenging Lighting Conditions[J]. IEEE Transactions on intelligent Transaction systems (S1524-9050).2015, 16(5): 2723-2743.
[12] Haris S M, Zakaria M K, Nuawi M Z.Depth Estimation from Monocular Vision using Image Eidge Complexity[C]//Adavanced Intelligent Mechatronics(AMI), IEEE/ASME (S2320-8430), 2011: 868-873.
[13] Wang Wenfeng, Yang Shujun, Li Yong, et al.A rough vehicle distance measurement method using monocular vision and license plate[J]. The 5th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems June 8-12, 2015, Shenyang, China (S0976-3252). 2015: 426-430.
[14] 周春樵, 朱思征, 徐烨. 一种改进的汽车安全距离计算仿真系统[J]. 信息技术. 2016(2): 189-193.
Zhou chunqiao, Zhu sizheng, Xu ye. An improved vehicle safety distance computation simulation system[J]. Information technique. 2016(2): 189-193.
[15] 杨炜, 巩建强, 魏朗, 等. 融合道路图像关键信息的车距视觉测量方法[J]. 计算机工程与应用, 2015, 51(23): 6-10.
Yang wei, Gong jianqiang, Wei lang,etc., Vehicle distance vision measurement method fusion with road image key information[J]. Computer engineering and application, 2015, 51(23): 6-10.
[16] 陈勇, 陈瑶. 基于单目视觉的前方车辆距离计算方法研究[J]. 机电工程, 2014, 31(11): 1499-1504.
Chen yong, Chen yao. Calculation method of preceding vehicle distance based on monocular vision[J]. Mechanical & Electrical Engineering, 2014, 31(11): 1499-1504.
[17] 余厚云, 张为公. 基于单目视觉的跟驰车辆车距测量方法[J]. 东南大学学报(自然科学版), 2012, 42(3): 542-546.
Yu houyun, Zhang weigong. Method of vehicle distance measurement for following car based on monocular vision[J]. Journal of Southeast University (Natural Science Edition), 2012, 42(3): 542-546.
[18] 金立生, 王岩, 刘景华, 等. 基于AdaBoost算法的日间前方车辆检测[J]. 吉林大学学报(工学版), 2014, 44(6):1604-1608.
Jin lisheng, Wang yan, Liu Jinghua, etc. Front vehicle detection based on Adaboost algorithm in daytime[J]. Journal of Jilin University(Engineering and Technology Edition) 2014, 44(6):1604-1608.