Vision-based Road Sign Detection

In this paper, we present a stereo-vision based approach for road sign detection. As opposed to traffic signs, which are typically made up of well-defined pictographs, road signs can contain arbitrary information. Here, color... [ view full abstract ]

In this paper, we present a stereo-vision based
approach for road sign detection. As opposed to traffic signs,
which are typically made up of well-defined pictographs, road
signs can contain arbitrary information. Here, color and shape
are the main two cues that represent different classes of road
signs, e.g. signs on the highway vs. signs on country roads.

To that extent, the proposed model couples efficient low-level
color-based segmentation in HSL space with higher-level
constraints that integrate prior knowledge on sign geometry
in 3D through stereo-vision. Additional robustness is obtained by
temporal integration as well as by matching detected signs against
the results of object detectors for other traffic participants.

The effectiveness of our approach is demonstrated on a real-world
stereo-vision dataset (3700 images) that has been captured
from a moving vehicle on German highways and country roads.
Our results indicate competitive performance at real-time speeds.