Additional traffic sign detection using learned corner representations

The detection of traffic signs and recognizing their meanings is crucial for applications such as online detection in automated driving or automated map data updates. Despite all progress in this field detecting and recognizing additional traffic signs, which may invalidate main traffic signs, has been widely disregarded in the scientific community. As a continuation of our earlier work we present a novel high-performing additional sign detector here, which outperforms our recently published state-of-the-art results significantly. Our approach relies on learning corner area representations using Aggregated Channel Features (ACF). Subsequently, a quadrangle generation and filtering strategy is applied, thus effectively dealing with the large aspect ratio variations of additional signs. It yields very high detection rates on a challenging dataset of high-resolution images captured with a windshield-mounted smartphone, and offers very precise localization while maintaining real-time capability. More than 95% of the additional traffic signs are detected successfully with full content detection at a false positive rate well below 0.1 per main sign, thus contributing a small step towards enabling automated driving.