Classifying Traffic Scenes Using The GIST Image Descriptor

TL;DR

This paper introduces a new traffic scene dataset and evaluates the GIST image descriptor's effectiveness in classifying scenes with minimal features, demonstrating promising recognition performance.

Contribution

The paper presents the FM1 dataset and assesses GIST descriptors for traffic scene classification in low bandwidth scenarios, showing their potential effectiveness.

Findings

GIST descriptor achieved high recognition rates in traffic scene classification.

The FM1 dataset contains 5615 images across eight traffic scene categories.

GIST descriptors are suitable for low-bandwidth traffic scene recognition.

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.