• DocumentCode
    2948289
  • Title

    Intelligent Vehicle Detection and Tracking for Highway Driving

  • Author

    Xu, Wanxin ; Qiu, Meikang ; Chen, Zhi ; Su, Hai

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Kentucky, Lexington, KY, USA
  • fYear
    2012
  • fDate
    9-13 July 2012
  • Firstpage
    67
  • Lastpage
    72
  • Abstract
    Due to the increment of vehicles, the traffic jamming in cities becomes a serious challenge and the safety of people is threatened. Intelligent transportation system (ITS) and intelligent vehicles are critical to the efficiency of city transportation. In the area related with ITS and intelligent vehicles, moving vehicle detection and tracking are the most challenging problems. In this paper, we propose a framework for vehicle detection and tracking and make an in-depth research in key algorithms and techniques. We also conduct a serial of experiments on the basis of the existing results. Experimental results show that our proposed approach is feasible and effective for vehicle detection and tracking.
  • Keywords
    automated highways; road traffic; support vector machines; traffic engineering computing; transportation; city transportation efficiency; highway driving; intelligent transportation system; intelligent vehicle detection; intelligent vehicle tracking; support vector machine; traffic jamming; Equations; Feature extraction; Histograms; Mathematical model; Support vector machines; Target tracking; Vehicles; HOG Feature; Mean Shift; Part-based Model; SVM; Vehicle Detection; Vehicle Tracking;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia and Expo Workshops (ICMEW), 2012 IEEE International Conference on
  • Conference_Location
    Melbourne, VIC
  • Print_ISBN
    978-1-4673-2027-6
  • Type

    conf

  • DOI
    10.1109/ICMEW.2012.19
  • Filename
    6266233