A Novel Disparity Transformation Algorithm for Road Segmentation

TL;DR

This paper introduces a novel disparity transformation algorithm that improves road segmentation accuracy in stereo camera systems by making road disparities more uniform, utilizing efficient estimation methods and thresholding techniques.

Contribution

The paper proposes a new disparity transformation method with efficient parameter estimation to enhance road segmentation accuracy from stereo disparity maps.

Findings

Improved accuracy in road area extraction demonstrated through experiments.

Efficient estimation of roll angle and disparity parameters using golden section search and dynamic programming.

Enhanced segmentation precision with Otsu's thresholding on transformed disparity maps.

Abstract

The disparity information provided by stereo cameras has enabled advanced driver assistance systems to estimate road area more accurately and effectively. In this paper, a novel disparity transformation algorithm is proposed to extract road areas from dense disparity maps by making the disparity value of the road pixels become similar. The transformation is achieved using two parameters: roll angle and fitted disparity value with respect to each row. To achieve a better processing efficiency, golden section search and dynamic programming are utilised to estimate the roll angle and the fitted disparity value, respectively. By performing a rotation around the estimated roll angle, the disparity distribution of each row becomes very compact. This further improves the accuracy of the road model estimation, as demonstrated by the various experimental results in this paper. Finally, the Otsu's thresholding method is applied to the transformed disparity map and the roads can be accurately segmented at pixel level.