• DocumentCode
    2654386
  • Title

    Vehicle segmentation by edge classification method and the S-T MRF model

  • Author

    Inoue, Hiroshi ; Liu, Mingzhe ; Kamijo, Shunsuke

  • Author_Institution
    Tokyo Univ.
  • fYear
    2006
  • fDate
    17-20 Sept. 2006
  • Firstpage
    1543
  • Lastpage
    1549
  • Abstract
    In this paper, we propose a tracking algorithm, which is based on the collaboration of the S-T MRF model and a dedicated segmentation algorithm. Although the S-T MRF model was designed to be robust against occlusion, it regards vehicles that move in parallel occluding each other from the beginning to the end of the traffic images as a single region. In order to compensate such a defect of S-T MRF, we have developed a dedicated segmentation algorithm which decides boundaries of vehicles contained in such a single region by referring to the difference of edge patterns among the vehicles. By the experiments using traffic video from three different angles at different locations, our method was proved to be very successful
  • Keywords
    Markov processes; edge detection; image classification; image segmentation; random processes; traffic engineering computing; S-T MRF model; edge classification; spatio-temporal Markov random field; tracking algorithm; traffic images; traffic video; vehicle segmentation; Cameras; Image segmentation; Image sensors; Labeling; Lighting; Monitoring; Pixel; Robustness; Surveillance; Vehicles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE
  • Conference_Location
    Toronto, Ont.
  • Print_ISBN
    1-4244-0093-7
  • Electronic_ISBN
    1-4244-0094-5
  • Type

    conf

  • DOI
    10.1109/ITSC.2006.1707443
  • Filename
    1707443