Disparity image segmentation for free-space detection

The paper introduces a novel and efficient algorithm for determining the free-space in road driving assistance scenarios. The input data for the algorithm is gathered from a stereo camera and is processed as a disparity image. Each column of the disparity image is segmented based on its relative extreme points. The idea is inspired from a time series compression article which presents a method for segmenting data measured at equal intervals of time (time series): electro cardiograms, monthly stocking-exchanges, etc. The novelty of the method consists in adapting an idea used in a different area of interest for an image recognition purpose. Compared to existing algorithms in the driving assistance field that share the same goal, the proposed method achieves great adaptability and a linear time performance. The adaptability of the method is worth mentioning as it gives good results both on precise data gathered with a lidar scanner and on noisy disparity inferred with a stereo camera. The algorithm filters most of the errors of measurement while preserving the points of interest that delimit the road, objects or sky. Because the filtering steps preserve the data of interest, additional post-processing steps are no longer required thus minimizing the time complexity.