Towards real-time obstacle detection using a hierarchical decomposition methodology for stereo matching with a genetic algorithm

Abstract

This work is concerned with the stereo matching problem for real-time obstacle detection. The correspondence problem is viewed as an optimization task where the objective is to find a solution for which the matches are as compatible as possible with respect to specific constraints. The optimization process is performed by means of a genetic algorithm with a new encoding scheme. For an effective exploitation of the genetic algorithm for real-time obstacle detection, a multilevel searching strategy is proposed in order to speed-up the stereo matching process. The multilevel searching strategy consists of matching the edges at different levels by considering their gradient magnitudes. The performance of the proposed multilevel genetic stereo matching procedure is evaluated for real-time obstacle detection in front of a moving vehicle using linear stereo vision.