基于组合前景提取的混合交通两轮车辆视频检测

交通运输系统工程与信息 ›› 2014, Vol. 14 ›› Issue (5): 49-54.

• 智能交通系统与信息技术 • 上一篇下一篇

基于组合前景提取的混合交通两轮车辆视频检测

邝先验^{* 1, 2}，王成坤¹，许伦辉^{1, 2}

1. 江西理工大学电气工程与自动化学院, 江西赣州341000；2. 华南理工大学土木与交通学院,广州510640

收稿日期:2013-10-10 修回日期:2014-05-19 出版日期:2014-10-25 发布日期:2014-12-17
作者简介:邝先验（1976-），男，江西南康人，副教授.
基金资助:
国家自然科学基金(51268017，61263024)；江西省自然科学基金(2010GQS0076)；江西省教育厅科技项目 (GJJ13427).

Video-based Two-wheel Vehicle Detection for Mixed Traffic Flow Based on Combination Foreground Extraction

KUANG Xian-yan¹，WANG Cheng-kun¹，XU Lun-hui^1,2

1. School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, Jiangxi, China; 2. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China

Received:2013-10-10 Revised:2014-05-19 Online:2014-10-25 Published:2014-12-17

摘要/Abstract

摘要：

针对混合交通流中两轮车辆视频检测问题，提出一种基于混合高斯模型（GMM）与背景累加模型（BAM）的组合前景提取方法，该方法将GMM与BAM组合得到的2 种前景图像分别经过滤波和形态学的膨胀操作处理，然后进行“与”操作，过滤掉高斯前景中的大量噪声，提取出感兴趣前景区域.针对两轮车辆的轮廓边缘特征，采用Canny 边缘检测提取边缘信息，去除前景区域中的非目标区域，采用两轮车辆的自建模板，通过欧氏距离进行模板匹配，定位并标记感兴趣区域中的目标区域.在OpenCV 和Matlab7.1 实验测试平台上，对典型城市混合交通路段的交通流视频进行测试.结果表明，该方法对混合交通流中两轮车辆的识别检测具有较高的准确率.

关键词: 智能交通, 两轮车辆视频检测, 组合前景提取, 模板匹配, 混合交通

Abstract:

A two-wheel vehicle detection method for mixed traffic flow based on combination foreground extraction method is proposed. The foreground images extracted by Gausssian Mixture Model (GMM) and Background Accumulate Model (BAM) are carried out with“and”operation after the operations of filtration and expansion of morphology, which can filter out a lot of noise in the GMM foreground and extract the interested foreground fields. According to the contour edge character of two-wheel vehicles, the Canny edge detection method is used to extract the edge information. The self-built template and Euclidean distance are used in pattern matching in order to locate and mark the target area. The experiments are performed using the traffic flow video in the classical urban mixed traffic road. The results show that this method has high accuracy of two-wheel vehicle detection.

Key words: intelligent transportation, video- based two-wheel vehicle detection, combination foreground extraction, pattern matching, mixed traffic

中图分类号:

U491. 123

邝先验，王成坤，许伦辉. 基于组合前景提取的混合交通两轮车辆视频检测[J]. 交通运输系统工程与信息, 2014, 14(5): 49-54.

KUANG Xian-yan，WANG Cheng-kun，XU Lun-hui. Video-based Two-wheel Vehicle Detection for Mixed Traffic Flow Based on Combination Foreground Extraction[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(5): 49-54.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/abstract/abstract18851.shtml

http://www.tseit.org.cn/CN/Y2014/V14/I5/49

参考文献

[1] 王坤峰, 李镇江, 汤淑明. 基于多特征融合的视频交通数据采集方法[J]. 自动化学报, 2011, 37(3):322-330. [WANG K F, LI Z J, TANG S M. Visual traffic data collection approach based on multi- features fusion[J]. Acta Automatica Sinica, 2011, 37(3):322-330.]

[2] Jazayeri A, Cai H, Zheng J Y, et al. Vehicle detection and tracking in car video based on motion model[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(2): 583-595.

[3] 文学志, 方巍, 郑钰辉. 一种基于类Haar特征和改进 AdaBoost分类器的车辆识别算法[J]. 电子学报, 2011, 39(5): 1121-1126. [WEN X Z, FANG W, ZHENG Y H. An algorithm based on haar-like features and improved adaboost classifier for vehicle recognition[J]. Acta Electronica Sinica, 2011, 39(5): 1121-1126.]

[4] Kuo Y C, Pai N S, Li Y F. Vision- based vehicle detection for a driver assistance system[J]. Computers & Mathematics with Applications, 2011, 61(8):2096-2100.

[5] 钱志明, 杨家宽, 段连鑫. 基于视频的车辆检测与跟踪研究进展[J]. 中南大学学报(自然科学版), 2013,44 (S2): 222- 227. [QIAN Z M, YANG J K, DUAN L X. Research advances in video- based vehicle detection and tracking[J]. Journal of Central South University (Science and Technology), 2013,44(S2): 222-227.]

[6] Chiverton J. Helmet presence classification with motorcycle detection and tracking[J]. IET Intelligent Transport Systems, 2012, 6(3):259-269.

[7] 赵熠, 邵春福, 岳昊, 等. 基于视频图像处理的行人和非机动车数据采集技术[J]. 北京交通大学学报:自然科学版, 2007, 31(6):10 - 14. [ZHAO Y, SHAO C F, YUE H, et al. Study on vision-based image processing for real - time traffic data collection technique of pedestrians and cyclists[J]. Journal of Beijing Jiaotong University, 2007, 31(6):10-14.]

[8] 岳昊, 邵春福, 赵熠, 等. 基于视频序列的行人多目标跟踪方法研究[J]. 交通运输系统工程与信息, 2007, 7 (4): 47- 51. [YUE H, SHAO C F, ZHAO Y, et al. A Study on moving pedestrian tracking based on video sequences [J] . Journal of Transportation Systems Engineering and Information Technology, 2007, 7(4): 47-51.]

[9] Kanhere N K, Birchfield S T, Sarasua W A, et al. Traffic monitoring of motorcycles during special events using video detection[J], Transportation Research Record: Journal of the Transportation Research Board, 2010, 2160(127):69-76.

[10] Gibson D R P, Ling B, Tiwari S. Detecting motorcyclists and bicyclists at intersections[J]. Federal Highway Administration, 2010,73(6):34-38.

[11] Ku M Y, Chiu C C, Chen H T, et al. Visual motorcycle detection and tracking algorithms[J].Wseas Transactions on Electronics, 2008, 5(4):121-131.

[12] 王永忠, 梁彦, 潘泉, 等. 基于自适应混合高斯模型的时空背景建模[J]. 自动化学报, 2009, 35(4): 371-378. [WANG Y Z, LIANG Y, PAN Q, et al. Spatio-temporal background modeling based on adaptive mixture of gaussians[J]. Acta Automatica Sinica, 2009, 35(4): 371- 378.]

[13] 李刚,曾锐利,林凌,等.基于数学形态学的车牌定位算法[J]. 仪器仪表学报，2007,07:1323- 1327. [LI G, ZENG R L, LIN L, et al. Car license plate location algorithm based on mathematical morphology[J]. Chinese Journal of Scientific Instrument, 2007, 27(7): 1323-1327.]

[14] Medina-Carnicer R, Muñoz-Salinas R, Yeguas-Bolivar E, et al. A novel method to look for the hysteresis thresholds for the Canny edge detector[J]. Pattern Recognition, 2011, 44(6):1201-1211.

[15] 张建伟, 张启衡. 基于直线边缘特征匹配的扩展目标跟踪方法[J]. 光学学报, 2009,10:2826-2831. [Zhang J W, Zhang Q H. Extended object tracking method based on straight-line edge feature matching[J]. Acta Optica Sinica, 2009,10:2826-2831.]

基于组合前景提取的混合交通两轮车辆视频检测

Video-based Two-wheel Vehicle Detection for Mixed Traffic Flow Based on Combination Foreground Extraction

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	郭烈, 胥林立, 秦增科, 王旭. 自动驾驶接管影响因素分析与研究进展[J]. 交通运输系统工程与信息, 2022, 22(2): 72-90.
[2]	徐猛, 刘涛, 钟绍鹏, 姜宇. 城市智慧公交研究综述与展望[J]. 交通运输系统工程与信息, 2022, 22(2): 91-108.
[3]	程君, 张立炎, 陈启宏. 一种基于地图辅助的自动驾驶视-惯融合定位方法[J]. 交通运输系统工程与信息, 2022, 22(2): 117-126.
[4]	王国栋, 刘立, 孟宇, 马智萍, 郑淏清, 顾青, 白国星. 一体式车辆避撞轨迹规划与跟踪控制[J]. 交通运输系统工程与信息, 2022, 22(2): 127-136.
[5]	马东方, 陈曦, 吴晓东, 金盛. 基于强化学习的干线信号混合协同优化方法[J]. 交通运输系统工程与信息, 2022, 22(2): 145-153.
[6]	许波桅, 王玲玲, 李军军. 自动化码头多级作业超网络延误传导特性研究[J]. 交通运输系统工程与信息, 2022, 22(2): 280-289.
[7]	田会娟, 刘嘉伟, 翟佳豪, 邓琳琳. 基于多入侵线的视频车速检测方法[J]. 交通运输系统工程与信息, 2022, 22(1): 49-56.
[8]	贾彦峰, 曲大义, 赵梓旭, 王韬, 宋慧. 基于安全势场的网联自主车辆跟驰行为决策及模型[J]. 交通运输系统工程与信息, 2022, 22(1): 85-97.
[9]	刘文, 汪文博. 基于秩最小化矩阵去噪的船舶轨迹重构方法[J]. 交通运输系统工程与信息, 2022, 22(1): 106-114.
[10]	赵建东, 陈溱, 焦彦利, 张凯丽, 韩明敏. 重点营运车辆的异常驾驶行为识别研究[J]. 交通运输系统工程与信息, 2022, 22(1): 282-291.
[11]	徐东伟, 彭航, 商学天, 魏臣臣, 杨艳芳. 基于图自编码-生成对抗网络的路网数据修复[J]. 交通运输系统工程与信息, 2021, 21(6): 33-41.
[12]	游峰, 梁健中, 曹水金, 肖智豪, 吴镇江, 王海玮. 面向多目标跟踪的密集行人群轨迹提取和运动语义感知[J]. 交通运输系统工程与信息, 2021, 21(6): 42-54.
[13]	王福建, 俞佳浩, 赵锦焕, 梅振宇. 基于站点实时关联度的短时公交客流预测方法[J]. 交通运输系统工程与信息, 2021, 21(6): 131-144.
[14]	张毅, 姚丹亚, 李力, 裴华鑫, 晏松, 葛经纬. 智能车路协同系统关键技术与应用[J]. 交通运输系统工程与信息, 2021, 21(5): 40-51.
[15]	龙建成, 郭嘉琪. 动态交通分配问题研究回顾与展望[J]. 交通运输系统工程与信息, 2021, 21(5): 125-138.