Real-time obstacle detection is an essential function for the future of Advanced Driver Assistance Systems (ADAS), but its applications to the driving safety require a very high reliability: the detection rate must be high, while the false detection rate must remain extremely low. Such features seem antinomic for obstacle detection systems, especially when using a single sensor. Multi-sensor fusion is often considered as a mean to reduce this limitation. In this paper, we propose to use stereo-vision as a post-process to improve the reliability of any obstacle detection system, by reducing the number of false positives. Our algorithm, which is both generic and real-time confirms detections by locally using the stereoscopic data. We evaluated and validated our approach with an initial detection based on a vision system and a laser scanner. The evaluation dataset is real on-road data and contains more than 20000 images.