Classifying Traffic Scenes using the GIST Image Descriptor

Abstract

This paper investigates classification of traffic scenes in a very low bandwidth scenario, where an image should be coded by a small number of features. We introduce a novel dataset, called the FM1 dataset, consisting of 5615 images of eight different traffic scenes: open highway, open road, settlement, tunnel, tunnel exit, toll booth, heavy traffic and the overpass. We evaluate the suitability of the GIST descriptor as a representation of these images, first by exploring the descriptor space using PCA and k-means clustering, and then by using an SVM classifier and recording its 10-fold cross-validation performance on the introduced FM1 dataset. The obtained recognition rates are very encouraging, indicating that the use of the GIST descriptor alone could be sufficiently descriptive even when very high performance is required.

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BibTeX Citation

DOI

10.20532/ccvw.2013.0009

https://doi.org/10.20532/ccvw.2013.0009

BibTeX

@InProceedings{10.20532/ccvw.2013.0009,
  author =       {Ivan Sikiri{\' c} and Karla Brki{\' c} and Sini{\v
                  s}a {\v S}egvi{\' c}},
  title =        {Classifying Traffic Scenes Using The {GIST} Image
                  Descriptor},
  booktitle =    {Proceedings of the Croatian Compter Vision Workshop,
                  Year 1},
  pages =        {19-24},
  year =         2013,
  editor =       {Lon{\v c}ari{\' c}, Sven and {\v S}egvi{\' c},
                  Sini{\v s}a},
  address =      {Zagreb},
  month =        {September},
  organization = {Center of Excellence for Computer Vision},
  publisher =    {University of Zagreb},
  abstract =     {This paper investigates classification of traffic
                  scenes in a very low bandwidth scenario, where an
                  image should be coded by a small number of
                  features. We introduce a novel dataset, called the
                  FM1 dataset, consisting of 5615 images of eight
                  different traffic scenes: open highway, open road,
                  settlement, tunnel, tunnel exit, toll booth, heavy
                  traffic and the overpass. We evaluate the
                  suitability of the GIST descriptor as a
                  representation of these images, first by exploring
                  the descriptor space using PCA and k-means
                  clustering, and then by using an SVM classifier and
                  recording its 10-fold cross-validation performance
                  on the introduced FM1 dataset. The obtained
                  recognition rates are very encouraging, indicating
                  that the use of the GIST descriptor alone could be
                  sufficiently descriptive even when very high
                  performance is required.},
  doi =          {10.20532/ccvw.2013.0009},
  url =          {https://doi.org/10.20532/ccvw.2013.0009}
}