  Semantic road region segmentation is a high-level task, which paves the way
towards road scene understanding. This paper presents a residual network
trained for semantic road segmentation. Firstly, we represent the projections
of road disparities in the v-disparity map as a linear model, which can be
estimated by optimizing the v-disparity map using dynamic programming. This
linear model is then utilized to reduce the redundant information in the left
and right road images. The right image is also transformed into the left
perspective view, which greatly enhances the road surface similarity between
the two images. Finally, the processed stereo images and their disparity maps
are concatenated to create a set of 3D images, which are then utilized to train
our neural network. The experimental results illustrate that our network
achieves a maximum F1-measure of approximately 91.19% when analyzing the images
from the KITTI road dataset.
