• DocumentCode
    2692179
  • Title

    Vehicle detection from aerial imagery

  • Author

    Gleason, Joshua ; Nefian, Ara V. ; Bouyssounousse, Xavier ; Fong, Terry ; Bebis, George

  • Author_Institution
    Univ. of Nevada, Reno, NV, USA
  • fYear
    2011
  • fDate
    9-13 May 2011
  • Firstpage
    2065
  • Lastpage
    2070
  • Abstract
    Vehicle detection from aerial images is becoming an increasingly important research topic in surveillance, traffic monitoring and military applications. The system described in this paper focuses on vehicle detection in rural environments and its applications to oil and gas pipeline threat detection. Automatic vehicle detection by unmanned aerial vehicles (UAV) will replace current pipeline patrol services that rely on pilot visual inspection of the pipeline from low altitude high risk flights that are often restricted by weather conditions. Our research compares a set of feature extraction methods applied for this specific task and four classification techniques. The best system achieves an average 85% vehicle detection rate and 1800 false alarms per flight hour over a large variety of areas including vegetation, rural roads and buildings, lakes and rivers collected during several day time illuminations and seasonal changes over one year.
  • Keywords
    aircraft; computer vision; feature extraction; object detection; pipelines; remotely operated vehicles; telerobotics; UAV; aerial imagery; classification technique; feature extraction; gas pipeline threat detection; oil pipeline threat detection; pipeline patrol service; rural environment; unmanned aerial vehicle; vehicle detection; visual inspection; Feature extraction; Histograms; Image edge detection; Support vector machines; Training; Vehicle detection; Vehicles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Robotics and Automation (ICRA), 2011 IEEE International Conference on
  • Conference_Location
    Shanghai
  • ISSN
    1050-4729
  • Print_ISBN
    978-1-61284-386-5
  • Type

    conf

  • DOI
    10.1109/ICRA.2011.5979853
  • Filename
    5979853