• DocumentCode
    2689055
  • Title

    Vehicle Extraction and Speed Detection from Digital Aerial Images

  • Author

    Yamazaki, Fumio ; Liu, Wen ; Vu, T. Thuy

  • Author_Institution
    Grad. Sch. of Eng., Chiba Univ., Chiba
  • Volume
    3
  • fYear
    2008
  • fDate
    7-11 July 2008
  • Abstract
    A new object-based method is developed to extract the moving vehicles and subsequently detect their speeds from two consecutive digital aerial images automatically. Several parameters of gray values and sizes are examined to classify the objects in the image. The vehicles and their associated shadows can be discriminated by removing big objects such as roads. To detect the speed, firstly the vehicles and shadows are extracted from the two images. The corresponding vehicles from these images are linked based on the order, size, and their distance within a threshold. Finally, using the distance between the corresponding vehicles and the time lag between the two images, the moving speed can be detected. Our test shows a promising result of detecting the moving vehicles´ speeds. Further development will employ the proposed method for a pair of QuickBird panchromatic and multi-spectral images, which are at a coarser spatial resolution.
  • Keywords
    feature extraction; geophysical techniques; image classification; remote sensing; road vehicles; Japan; Minato-ku; QuickBird panchromatic images; Tokyo; coarser spatial resolution; digital aerial images; image classification; moving vehicle image extraction; multi-spectral images; object-based method; road vehicle; shadows image extraction; vehicle speed detection; Automatic testing; Automotive engineering; Bayesian methods; Cameras; Cities and towns; Data mining; Remote monitoring; Roads; Software testing; Vehicle detection; Digital aerial image; object-based method; speed detection; vehicle extraction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
  • Conference_Location
    Boston, MA
  • Print_ISBN
    978-1-4244-2807-6
  • Electronic_ISBN
    978-1-4244-2808-3
  • Type

    conf

  • DOI
    10.1109/IGARSS.2008.4779606
  • Filename
    4779606