Free-space detection using online disparity-supervised color modeling

This work contributes to vision processing for intelligent vehicle applications with an emphasis on Advanced Driver Assistance Systems (ADAS). A key issue for ADAS is the robust and efficient detection of free drivable space in front of the vehicle. To this end, we propose a stixel-based probabilistic color-segmentation algorithm to distinguish the ground surface from obstacles in traffic scenes. Our system learns color appearance models for free-space and obstacle classes in an online and self-supervised fashion. To this end, it applies a disparity-based segmentation, which can run in the background of the critical system path and at a lower frame rate than the color-based algorithm. This strategy enables an algorithm without a real-time disparity estimate. As a consequence, the current road scene can be analyzed without the extra latency of disparity estimation. This feature translates into a reduced response time from data acquisition to data analysis, which is a critical property for high-speed ADAS. Our evaluation over different color modeling strategies on publicly available data shows that the color-based analysis can achieve similar (77.6% vs. 77.3% correct) or even better results (4.3% less missed obstacle-area) in difficult imaging conditions, compared to a state-of-the-art disparity-only method.