Visual guard rail detection for advanced highway assistance systems

In this paper we present a novel method to detect guard rails in highway scenarios using a stereo camera setup. In contrast to previous methods, we combine geometry information with appearance cues using a state-of-the-art feature encoding method. In our system pipeline, we follow a hough-based approach to localize potential guard rails in the image and require each detected line to fulfill linearity in depth as well as certain height expectations. To leverage the appearance information, we exploit an efficient bag-of-features representation that relies on randomized clustering forests. The effectiveness of our approach is demonstrated on a large novel dataset with pixel-level annotations of guard rails in real-world highway scenarios.