Classifying Traffic Scenes Using The GIST Image Descriptor
This work addresses traffic scene classification for applications like autonomous driving or surveillance in bandwidth-limited scenarios, but it is incremental as it applies an existing descriptor to a new dataset.
The paper tackled the problem of classifying traffic scenes under low bandwidth constraints by evaluating the GIST image descriptor on a new FM1 dataset of 5615 images across eight scene types, achieving very encouraging recognition rates with SVM classification and 10-fold cross-validation.
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.