Volume 12 Issue 4
. 
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2012  >  12(4): 107-113
Next Previous
LOU Lu, ZHAO Ling, GENG Tao. Detecting and tracking method of moving vehicle[J]. Journal of Traffic and Transportation Engineering, 2012, 12(4): 107-113. doi: 10.19818/j.cnki.1671-1637.2012.04.014
Citation: LOU Lu, ZHAO Ling, GENG Tao. Detecting and tracking method of moving vehicle[J]. Journal of Traffic and Transportation Engineering, 2012, 12(4): 107-113. doi: 10.19818/j.cnki.1671-1637.2012.04.014
shu

Detecting and tracking method of moving vehicle

doi: 10.19818/j.cnki.1671-1637.2012.04.014
  • 1.

    School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China

  • 2.

    Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, Ceredigion, UK

More Information
  • Author Bio:

    LOU Lu(1969-), male, lecturer, +86-23-62652751, cloudlou@163.com

  • Received Date: 2012-02-13
  • Publish Date: 2012-08-25
    Abstract
  • In order to improve the comprehensive management ability of intelligent transportation systems in cities, a detecting and tracking method of moving vehicle was presented by using video analysis. Considering the pavement environment of urban transport artery and the difference between moving object and the statistical characteristics for road background, an adaptive background updating algorithm was realized based on Bayesian probability criterion, from which foreground image was extracted. Motion detection and real-time tracking were realized for target vehicle in video sequence based on Kalman filter. The traffic flow video collected from a certain urban transport artery of Chongqing was detected by using the proposed method. Experimental result indicates that the video with normal resolution can be processed in time by using the method, and the average detecting accuracy is 94 %, so the proposed method has good real-time performance and robustness, and meets the requirement of real- time detecting and tracking vehicles in urban traffic arteries. 2 tabs, 5 figs, 15 refs.

     

    • FullText(HTML)
  • loading
    • References(15)
  • [1]
    YANG Guo-liang, WANG Zhi-liang, MU Shi-tang, et al. An improved optical flow algorithm[J]. Computer Engineering, 2006, 32(15): 187-188, 226. (in Chinese) doi: 10.3969/j.issn.1000-3428.2006.15.066
    [2]
    ZHENG Jin, LI Bo. Prospects and current studies on motion object detection in video sequences[J]. Application Research of Computers, 2008, 25(12): 3534-3540. (in Chinese) doi: 10.3969/j.issn.1001-3695.2008.12.004
    [3]
    ZHA Cheng-dong, WANG Chang-song, CUI Wei. Application of background subtraction under complex scene[J]. Computer Engineering and Design, 2008, 29(4): 894-895. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-SJSJ200804040.htm
    [4]
    TIAN Jun, WEI Zhen-hua, WU Si-yuan. A self-adaptive background updating algorithm of energy method[J]. Journal of Frontiers of Computer Science and Technology, 2009, 3(2): 218-224. (in Chinese) doi: 10.3778/j.issn.1673-9418.2009.02.010
    [5]
    SONG K T, TAI J C. Real-time background estimation of traffic imagery using group-based histogram[J]. Journal of Information Science and Engineering, 2008(24): 411-423.
    [6]
    TANIGUCHI H, NAKAMURA T, FURUSAWA H. Methods of traffic flow measurement using spatio-temporal image[C]∥ IEEE. Proceedings of 1999 International Conference on Image processing. Kobe: IEEE, 1999: 16-20.
    [7]
    WAKABAYASHI Y, AOKI M. Traffic flow measurement using stereo slit camera[C]∥IEEE. Proceedings of the 7th international Conference on Intelligent Transportation Systems. Washington DC: IEEE, 2004: 7-12.
    [8]
    KOLLER D, WEBER J, HUANG T, et al. Towards robust automatic traffic scene analysis in real-time[C]∥IEEE. Proceeding of the 33rd of IEEE Conference on Pattern Recognition. Jerusalem: IEEE, 1994: 126-131.
    [9]
    JUN G, AGGARWAL J K, GOKMEN M. Tracking and segmentation of highway vehicles in cluttered and crowded scenes[C]∥IEEE. Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision. Copper Mountain: IEEE, 2008: 1-6.
    [10]
    ZHAO Yu. The research of object segmentation and tracking in video processing[D]. Beijing: Chinese Academy of Sciences, 2004. (in Chinese)
    [11]
    TAMERSOY B, AGGARWAL J K. Robust vehicle detection for tracking in highway surveillance videos using unsupervised learning[C]∥IEEE. Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance. Genova: IEEE, 2009: 529-534.
    [12]
    LI Li-yuan, HUANG Wei-min, GUI Y, et al. Statistical modeling of complex backgrounds for foreground object detection[J]. IEEE Transactions on Image Processing, 2004, 13(11): 1459-1472. doi: 10.1109/TIP.2004.836169
    [13]
    YUAN Ji-wei, SHI Zhong-ke. A method of vehicle tracking based on GM(1, 1)[J]. Control and Decision, 2006, 21(3): 300-304. (in Chinese) doi: 10.3321/j.issn:1001-0920.2006.03.014
    [14]
    KALMAN R E. A new approach to linear filtering and prediction problems[J]. Transactions of the ASME—Journal of Basic Engineering, 1960(82): 35-45.
    [15]
    STORVIK G. Particle filters for state space models with the presence of unknown static parameters[J]. IEEE Transactions on Signal Processing, 2002, 50(2): 281-289. doi: 10.1109/78.978383
    • Relative Articles
    • Supplements(0)
    • Cited By

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1642) PDF downloads(1257) Cited by()
    Related

    Website copyright Journal of Traffic and Transportation Engineering Editorial Department陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang'an University, Middle Section of South 2nd Ring Road, Xi'an, China（710064） Tel：029-82334388 Email：jygc@gfscw.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return