  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.
