[1]王传钦[],曹江涛[],姬晓飞[]. 基于视频分析技术的车距测量及预警系统设计[J].计算机技术与发展,2016,26(09):87-90.
　WANG Chuan-qin[],CAO Jiang-tao[],JI Xiao-fei[]. Design of a Vehicle Distance Measurement and Early Warning System Based on Video Analysis Techniques[J].,2016,26(09):87-90.
点击复制

 基于视频分析技术的车距测量及预警系统设计()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年09期
页码:
87-90
栏目:
应用开发研究
出版日期:
2016-09-10

文章信息/Info

Title:
 Design of a Vehicle Distance Measurement and Early Warning System Based on Video Analysis Techniques
文章编号:
1673-629X（2016)09-0087-04
作者:
 王传钦[1]; 曹江涛[1]; 姬晓飞[2]
 1.辽宁石油化工大学 信息与控制工程学院；2.沈阳航空航天大学 自动化学院
Author(s):
 WANG Chuan-qin[1]; CAO Jiang-tao[1]; JI Xiao-fei[2]
关键词:
 ; 车辆检测; Haar-like特征; Adaboost算法; 跟踪 ; RBF神经网络
Keywords:
 ; vehicle detection; Haar-like feature; Adaboost algorithm; tracking; RBF neural network
分类号:
TP302
文献标志码:
A
摘要:
 车距测量及预警是汽车主动安全技术中的一个重要组成部分，而基于视觉的车距测量及预警系统一直是智能车系统和辅助安全系统中研究的热点。为了提高车距测量的精确度和实时性，以Visual C++6.0集成开发环境和OpenCV开源计算机视觉库为实验平台，设计并实现了一种基于视频分析技术的车距测量及预警系统。该系统具有车辆检测、车辆跟踪、距离测量及预警等功能。以Haar-like特征作为图像描述，结合Adaboost算法训练分类器实现道路中车辆的检测；采用CamShift和Kalman相结合的方法实现目标车辆的跟踪及预测；提出一种基于RBF神经网络的车距测量及预测方法。实验结果表明，该系统能较准确地实现1~15 m范围内的车辆检测及车距测量，且具有良好的实时性。
Abstract:
 Vehicle distance measuring and early warning is an important component in vehicle active safety technology. And the vision-based vehicle distance measuring and early warning system has been a research hotspot of the intelligent vehicle system and secondary safety system. In order to improve the accuracy and processing speed of vehicle distance measurement,vehicle distance and early warning system is designed and implemented based on video analysis techniques in Visual C++6. 0 and OpenCV software environment,which has functions like vehicle detection,vehicle tracking,distance measurement and early warning and so on. Haar-like features is chosen as im-age descriptions,and Adaboost algorithm is combined to train classifiers to achieve vehicles detection. A combination method of CamShift and Kalman is used to track the target vehicle,and the measurement and prediction of distance is achieved by using RBF neural network. Experiments show that the system can accurately realize the range of 1~15 m for vehicle detection and distance measurement,and it has good real-time performance.

相似文献/References:

[1]方宏 杜正春.车辆检测线远程监控系统的研制[J].计算机技术与发展,2009,(12):185.
　FANG Hong,DU Zheng-chun.Development of Remote Monitoring System of Vehicle Inspection Lane[J].,2009,(09):185.
[2]张明恒 王华莹 郭烈.基于改进K—Means算法的车辆识别方法[J].计算机技术与发展,2012,(05):53.
　ZHANG Ming-heng,WANG Hua-ying,GUO Lie.Method of Vehicle Detection Based on Improved K-Means Algorithm[J].,2012,(09):53.
[3]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(09):1.
[4]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(09):5.
[5]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(09):13.
[6]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(09):21.
[7]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(09):25.
[8]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(09):29.
[9]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(09):34.
[10]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(09):38.

更新日期/Last Update: 2016-10-25