From corners to rectangles — Directional road sign detection using learned corner representations

In this work we adopt a novel approach for the detection of rectangular directional road signs in single frames captured from a moving car. These signs exhibit wide variations in sizes and aspect ratios and may contain arbitrary information, thus making their detection a challenging task with applications in traffic sign recognition systems and vision-based localization. Our proposed approach was originally presented for additional traffic sign detection in small image regions and is generalized to full image frames in this work. Sign corner areas are detected by four ACF-detectors (Aggregated Channel Features) on a single scale. The resulting corner detections are subsequently used to generate quadrangle hypotheses, followed by an aggressive pruning strategy. A comparative evaluation on a database of 1500 German road signs shows that our proposed detector outperforms other methods significantly at close to real-time runtimes and yields thrice the very low error-rate of the recent MS-CNN framework while being two orders of magnitude faster.