A Novel Disparity Transformation Algorithm for Road Segmentation
This work addresses road segmentation for advanced driver assistance systems, presenting an incremental improvement in processing efficiency and accuracy.
The paper tackles road segmentation from stereo camera disparity maps by introducing a disparity transformation algorithm that makes road pixel disparities similar, using roll angle and fitted disparity parameters estimated via golden section search and dynamic programming, resulting in improved accuracy as shown in experimental results.
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.