Road Detection by One-Class Color Classification: Dataset and Experiments
This work addresses the problem of road detection for autonomous vehicles, but it is incremental as it builds on existing color-based classification methods.
The paper tackled road detection for autonomous driving by introducing a new dataset of road images and evaluating various classifiers and color representations, achieving an exhaustive evaluation but without reporting specific performance numbers.
Detecting traversable road areas ahead a moving vehicle is a key process for modern autonomous driving systems. A common approach to road detection consists of exploiting color features to classify pixels as road or background. These algorithms reduce the effect of lighting variations and weather conditions by exploiting the discriminant/invariant properties of different color representations. Furthermore, the lack of labeled datasets has motivated the development of algorithms performing on single images based on the assumption that the bottom part of the image belongs to the road surface. In this paper, we first introduce a dataset of road images taken at different times and in different scenarios using an onboard camera. Then, we devise a simple online algorithm and conduct an exhaustive evaluation of different classifiers and the effect of using different color representation to characterize pixels.