• DocumentCode
    2634786
  • Title

    Efficient image gradient-based object localisation and recognition

  • Author

    Tan, T.N. ; Sullivan, G.D. ; Baker, K.D.

  • Author_Institution
    Dept. of Comput. Sci., Reading Univ., UK
  • fYear
    1996
  • fDate
    18-20 Jun 1996
  • Firstpage
    397
  • Lastpage
    402
  • Abstract
    This paper reports novel algorithms for the efficient localisation and recognition of vehicles in traffic scenes, which eliminate the need for explicit symbolic feature extraction and matching. The algorithms make use of two a priori sources of knowledge about the scene and the objects: (i) the ground-plane constraint, and (ii) the fact that road vehicles are strongly rectilineal: The algorithms are demonstrated and tested using routine outdoor traffic images. Success with a variety of vehicles demonstrates the efficiency and robustness of context-based computer vision in road traffic scenes. The limitations of the algorithms are also addressed in the paper
  • Keywords
    computer vision; object recognition; road traffic; traffic engineering computing; computer vision; feature extraction; image gradient-based; object localisation; recognition of vehicles; road traffic scenes; traffic images; Computer science; Computer vision; Feature extraction; Image recognition; Land vehicles; Layout; Road vehicles; Robustness; Solid modeling; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition, 1996. Proceedings CVPR '96, 1996 IEEE Computer Society Conference on
  • Conference_Location
    San Francisco, CA
  • ISSN
    1063-6919
  • Print_ISBN
    0-8186-7259-5
  • Type

    conf

  • DOI
    10.1109/CVPR.1996.517103
  • Filename
    517103