一种利用隐式曲线族的车辆违章检测算法

doi:10.3969/j.issn.1001-2400.2016.02.024

西安电子科技大学学报 ›› 2016, Vol. 43 ›› Issue (2): 139-144.doi: 10.3969/j.issn.1001-2400.2016.02.024

一种利用隐式曲线族的车辆违章检测算法

马丽文1,2;郭玉坤1,2;李金屏1,2

(1. 济南大学信息科学与工程学院，山东济南 250022;
2. 山东省网络环境智能计算技术重点实验室，山东济南 250022)

收稿日期:2014-10-24 出版日期:2016-04-20 发布日期:2016-05-27
作者简介:马丽文(1988-)，女，济南大学硕士研究生，E-mail: 1195289418@qq.com．
基金资助:
国家自然科学基金青年基金资助项目(61203341)；山东省高等学校科技计划资助项目(J14LN15，J12LN19)；济南大学校基金重点资助项目(XKY1202)

Traffic violation detection based on the implicit curves family

MA Liwen1,2;GUO Yukun1,2;LI Jinping1,2

(1. School of Information Science and Engineering, Univ. of Ji'nan, Ji'nan 250022, China;
2. Shandong Provincial Key Lab. of Network Based Intelligent Computing, Ji'nan 250022, China)

Received:2014-10-24 Online:2016-04-20 Published:2016-05-27

摘要/Abstract

摘要：

针对城市道路交通，提出了一种基于隐式曲线族的车辆违章检测算法．隐式函数族用于描述道路上的各个不同区域．当检测到车辆在不同道路区域之间行驶时，利用隐式函数族可以快速方便地判断车辆所在区域．根据车辆行驶轨迹和隐式曲线族所描述的不同区域之间的位置关系，能够快速准确地判定车辆是否违章以及所违反的交通规则．在实际检测中，利用分段线性函数族来逼近各个隐式曲线，极大地提高了检测效率，进而提高了违章判断的准确性．实验结果表明，当车辆出现违章时，可以实时显示所违反的具体交通规则．

关键词: 智能交通, 违章检测, 隐式曲线族, 分段线性函数

Abstract:

We propose an effective algorithm for the detection of the traffic violation vehicle by employing a set of implicit function curves which can be used to describe the different regions on the roads. When the vehicles are detected to run across different road regions, we can determine in which region that vehicle lies easily and immediately by using the set of implicit functions. According to the vehicle trajectory and the relationship among different regions described by the implicit functions, we can quickly and accurately determine whether the vehicle violates the traffic rules. In order to improve the detection efficiency and accuracy greatly, we use the piecewise linear functions to approximate the set of implicit functions in actual detection. Experimental results show that our algorithm can detect the traffic violation and display the specific violated traffic rules in real time.

Key words: intelligent transportation, traffic violation detection, implicit curve family, piecewise linear functions

马丽文;郭玉坤;李金屏. 一种利用隐式曲线族的车辆违章检测算法[J]. 西安电子科技大学学报, 2016, 43(2): 139-144.

MA Liwen;GUO Yukun;LI Jinping. Traffic violation detection based on the implicit curves family[J]. Journal of Xidian University, 2016, 43(2): 139-144.

参考文献

［1］ SEMERTZIDIS T, DIMITROPOULOS K, KOUSIA A. et al. Video Sensor Network for Real-time Traffic Monitoring and Surveillance ［J］. Intelligent Transport Systems, 2010, 4(2): 103-112.
［2］刘万军, 陈虹宇, 姚雪, 等. 利用七宫格的遮挡车辆凹性检测与分割［J］. 中国图象图形学报, 2014, 19(1): 45-53.
LIU Wanjun, CHEN Hongyu, YAO Xue, et al. Detection and Segmentation of Occluded Vehicles Based on Seven Grids ［J］. Journal of Image and Graphics, 2014, 19(1): 45-53.
［3］ JARRAYA S K, GHRAB N B, HAMMAMI M, et al. Tracking Moving Objects in Road Traffic Sequences［C］//Lecture Notes in Computer Science: 7340 LNCS. Heidelberg: Springer Verlage, 2012: 406-414.
［4］李金屏, 王群, 吴翔, 等. 基于视频的车辆检测和牌照识别［J］. 济南大学学报(自然科学版), 2014, 28(2): 89-96.
LI Jinping, WANG Qun, WU Xiang, et al. Detection of Vehicle and Recognition of License Plate Based on Video［J］. Journal of University of Jinan(Science and Technology), 2014, 28(2): 89-96.
［5］ DU 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.
［6］ AL-GHAILI A M, MASHOHOR S, RAMLI A R, et al. Vertical-edge-based Car-license-plate Detection Method［J］. IEEE Transactions on Vehicular Technology, 2013, 62(1): 26-38.
［7］张洪斌, 黄山. 面向城市路口的高清晰智能监控系统研究［J］. 四川大学学报(工程科学版), 2012, 44(1): 224-228.
ZHANG Hongbin, HUANG Shan. Research on the High Definition Intelligent Surveillance for Urban Intersection［J］. Journal of Sichuan University(Engineering Science Edition), 2012, 44(1): 224-228.
［8］刘振华, 黄磊, 刘昌平. 一种基于视频图像处理的车辆违章检测算法［J］. 公路交通科技, 2012, 29(2): 106-114.
LIU Zhenhua, HUANG Lei, LIU Changping. A Vehicle Violation Detection Algorithm Based on Video Image Processing［J］. Journal of Highway and Transportation Research and Development, 2012, 29(2): 106-114.
［9］ AKOZ O, KARSLIGIL M E. Traffic Event Classification at Intersections Based on the Severity of Abnormality ［J］. Machine Vision and Applications, 2014, 25(3): 613-632.
［10］佟守愚. 基于视频技术的交通违章检测与识别理论及方法研究［D］. 长春: 吉林大学, 2006.
［11］ OCHE M, NOOR R M, AL-JAWFI A S, et al. An Automatic Speed Violation Detection Framework for VANETs［C］//Proceedings of the IEEE International Conference on RFID-Technologies and Applications. Washington: IEEE Computer Society, 2013: 1-6.
［12］ LI J P, LI J, HOU W D. Moving Object Tracking Based on Particle Filter and Its Application in Intelligent Transportation System［J］. Journal of Pattern Recognition ＆ Image Processing, 2013, 4(3): 389-398.

一种利用隐式曲线族的车辆违章检测算法

Traffic violation detection based on the implicit curves family

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

Metrics

本文评价

推荐阅读 0