吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (04): 866-870.doi: 10.7964/jdxbgxb201304004

• 论文 • 上一篇    下一篇

基于智能相机的混合交通流检测方法

魏巍1, 李志慧1, 赵永华2, 曲昭伟1, 江晟1, 柴婷婷1   

  1. 1. 吉林大学 交通学院, 长春 130022;
    2. 吉林大学 计算机公共教学中心,长春 130022
  • 收稿日期:2012-11-26 出版日期:2013-07-01 发布日期:2013-07-01
  • 通讯作者: 赵永华(1979-),女,讲师,博士.研究方向:车辆安全,视频检测.E-mail:zhaoyonghua@jlu.edu.cn E-mail:zhaoyonghua@jlu.edu.cn
  • 作者简介:魏巍(1978-),男,工程师,博士研究生.研究方向:交通视频检测.E-mail:weiwei@jlu.edu.cn
  • 基金资助:

    国家自然科学基金项目(50808092);中国博士后科学基金第三批特别资助项目(201003535);第四十六批中国博士后科学基金面上项目(20090461035);中央高校基本科研业务费项目(201103149);吉林省科技发展计划项目(20080432);吉林省"春苗"人才计划项目.

Developing a smart camera for mixed traffic flow detection

WEI Wei1, LI Zhi-hui1, ZHAO Yong-hua2, QU Zhao-wei1, JIANG Sheng1, CHAI Ting-ting1   

  1. 1. College of Transportation, Jilin University, Changchun 130022,China;
    2. Center of Computer Teaching and Research, Jilin University, Changchun 130022,China
  • Received:2012-11-26 Online:2013-07-01 Published:2013-07-01

RICH HTML

0   

PDF (PC)

285

摘要：

针对目前缺乏混合交通视频智能检测设备的问题,利用CCD传感器与DSP芯片构建了智能相机框架体系和硬件原型系统,采用背景初始化、背景模型与前景获取、物体分割、特征提取、多目标识别分类、摄像机参数标定等视频图像处理技术,构建了混合交通流检测软件方法体系,开发了相应的嵌入式算法系统,实现了混合交通流参数检测。在不同交通状态下的测试结果反映了该系统具有良好的性能,可实现混合交通流参数检测。本方法可为混合交通流智能相机的研发提供借鉴和参考。

关键词: 交通运输系统工程, 视频检测, 混合交通流, 智能相机

Abstract:

Smart cameras in detection of mixed traffic flow parameters are in shortage. This creates a huge load of communication between the cameras and monitoring center. To solve this problem, this paper proposes a method of hardware and software codesign and implementation of a smart camera platform to detect mixed traffic flow parameters. Charge-coupled Device (CCD) camera sensor and Digital Signal Processor (DSP) are used to construct the camera hardware prototype. The video processing methods including background initialization, background model, foreground obtaining, image segmentation, pattern extraction, multi-classification etc. are used to construct the software system. Experiments were conducted under different mixed traffic conditions to test the camera system. Results show that this system can accurately obtain mixed traffic flow parameters. This study provides a reference for the development of smart camera system for mixed traffic detection.

Key words: engineering of communication and transportation system, video detection, mixed traffic flow, smart camera

中图分类号: 

  • U121

[1] Bramberger M, Brunner J, Rinner B,et al. Real-time video analysis on an embedded smart camera for traffic surveillance//In: Real-Time and Embedded Technology and Applications Symposium, 2004 Proceedings RTAS 2004 10th IEEE,2004:174-181.

[2] Bramberger M, Roman P Pflugfelder, Maier Arnold,et al. A smart camera for traffic surveillance//Proceedings of the First Workshop on Intelligent Solutions in Embedded Systems; 2003; Vienna University of Technology, Austria; 2003:153-164.

[3] Arth C, Bischof H, Leistner C. TRICam-an embedded platform for remote traffic surveillance//Computer Vision and Pattern Recognition Workshop, 2006 CVPRW '06 Conference on, 2006:125.

[4] Wei Bei-na, Shi Yu,Ye Ge-tian ,et al. Developing a smart camera for road traffic surveillance//Multimedia Signal Processing, 2008 IEEE 10th Workshop on,2008:826-831.

[5] Houben Q, Czyz J, Diaz J C Tocino,et al. Feature-based stereo vision using smart cameras for traffic surveillance//Fritz M, Schiele B, Piater JH, eds. Computer Vision Systems, Proceedings; 2009:144-153.

[6] Macesic M, Jelaca V, Nino-Castaneda J O. et al. Real-time detection of traffic events using smart cameras//In: Proceedings of SPIE-IS and T Electronic Imaging-Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques,Burlingame, CA, United states, 2012.

[7] 李志慧, 张长海, 曲昭伟,等. 交通流视频检测中背景模型与阴影检测算法[J]. 吉林大学学报:工学版, 2006, 36(6): 993-997. Li Zhi-hui, Zhang Chang-hai, Qu Zhao-wei,et al. Background extraction model and shadow detection algorithm in traffic flow video detection[J]. Journal of Jilin University (Engineering and Technology Edition),2006, 36(6): 993-997.

[8] Stauffer Chris, Grimson W E L. Adaptive background mixture models for real-time tracking[J]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1999, 2(Compendex): 246-252.

[9] 李志慧, 张长海, 曲昭伟,等. 交通流视频检测中背景初始化算法[J]. 吉林大学学报:工学版, 2008,38(1): 148-151. Li Zhi-hui, Zhang Chang-hai, Qu Zhao-wei, et al. Background initialization algorithm in traffic flow video detection[J]. Journal of Jilin University (Engineering and Technology Edition),2008, 38(1): 148-151.

[10] 曲昭伟, 陈红艳, 李志慧,等. 基于单模板的二维场景重建方法[J]. 吉林大学学报:工学版,2007,37(5): 1159-1163. Qu Zhao-wei, Chen Hong-yan, Li Zhi-hui,et al. 2D view reconstruction method based on single calibration pattern[J]. Journal of Jilin University (Engineering and Technology Edition),2007, 37(5): 1159-1163.

[11] Cortes Corinna, Vapnik Vladimir. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273-297.

[12] Scholkopf B, Smola A J, Williamson R C,et al. New support vector algorithms[J]. Neural Computation, 2000, 12(5): 1207-1245.

[13] Scholkopf B, Platt J C, Shawe-Taylor J,et al. Estimating the support of a high-dimensional distribution[J]. Neural Computation, 2001, 13(7): 1443-1471.

[14] Rangel Pedro, Lozano Fernando, Garcia Elkin. Boosting of support vector machines with application to editing//ICMLA 2005: 4th International Conference on Machine Learning and Applications, Los Angeles, CA, United States: Inst of Elec and Elec Eng Computer Society, 2005:374-379.
[1] 陈永恒,刘芳宏,曹宁博. 信控交叉口行人与提前右转机动车冲突影响因素[J]. 吉林大学学报(工学版), 2018, 48(6): 1669-1676.
[2] 常山,宋瑞,何世伟,黎浩东,殷玮川. 共享单车故障车辆回收模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1677-1684.
[3] 曲大义,杨晶茹,邴其春,王五林,周警春. 基于干线车流排队特性的相位差优化模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1685-1693.
[4] 宗芳, 齐厚成, 唐明, 吕建宇, 于萍. 基于GPS数据的日出行模式-出行目的识别[J]. 吉林大学学报(工学版), 2018, 48(5): 1374-1379.
[5] 刘翔宇, 杨庆芳, 隗海林. 基于随机游走算法的交通诱导小区划分方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1380-1386.
[6] 钟伟, 隽志才, 孙宝凤. 不完全网络的城乡公交一体化枢纽层级选址模型[J]. 吉林大学学报(工学版), 2018, 48(5): 1387-1397.
[7] 刘兆惠, 王超, 吕文红, 管欣. 基于非线性动力学分析的车辆运行状态参数数据特征辨识[J]. 吉林大学学报(工学版), 2018, 48(5): 1405-1410.
[8] 宗芳, 路峰瑞, 唐明, 吕建宇, 吴挺. 习惯和路况对小汽车出行路径选择的影响[J]. 吉林大学学报(工学版), 2018, 48(4): 1023-1028.
[9] 栾鑫, 邓卫, 程琳, 陈新元. 特大城市居民出行方式选择行为的混合Logit模型[J]. 吉林大学学报(工学版), 2018, 48(4): 1029-1036.
[10] 陈永恒, 刘鑫山, 熊帅, 汪昆维, 谌垚, 杨少辉. 冰雪条件下快速路汇流区可变限速控制[J]. 吉林大学学报(工学版), 2018, 48(3): 677-687.
[11] 王占中, 卢月, 刘晓峰, 赵利英. 基于改进和声搜索算法的越库车辆排序[J]. 吉林大学学报(工学版), 2018, 48(3): 688-693.
[12] 李志慧, 胡永利, 赵永华, 马佳磊, 李海涛, 钟涛, 杨少辉. 基于车载的运动行人区域估计方法[J]. 吉林大学学报(工学版), 2018, 48(3): 694-703.
[13] 陈松, 李显生, 任园园. 公交车钩形转弯交叉口自适应信号控制方法[J]. 吉林大学学报(工学版), 2018, 48(2): 423-429.
[14] 苏书杰, 何露. 步行交通规划交叉路口行人瞬时动态拥塞疏散模型[J]. 吉林大学学报(工学版), 2018, 48(2): 440-447.
[15] 孟品超, 李学源, 贾洪飞, 李延忠. 基于滑动平均法的轨道交通短时客流实时预测[J]. 吉林大学学报(工学版), 2018, 48(2): 448-453.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发