Abstract: 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.

Abstract: The task of traffic sign recognition is often considered to be solved after almost perfect results have been achieved on some public benchmarks. Yet, the closely related recognition of additional traffic signs is still lacking a solution. Following up on our earlier work on detecting additional traffic signs given a main sign detection [1], we here propose a complete pipeline for recognizing the content of additional signs, including text recognition by optical character recognition (OCR). We assume a given additional sign detection, first classify its layout, then determine content bounding boxes by regression, followed by a multi-class classification step or, if necessary, OCR by applying a text sequence classifier. We evaluate the individual stages of our proposed pipeline and the complete system on a database of German additional signs and show that it can successfully recognize about 80% of the signs correctly, even under very difficult conditions and despite low input resolutions at runtimes well below 12ms per sign.

Abstract: 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.

Abstract: Automated driving is a long term goal that currently generates a lot of interest and effort in the scientific community and the industry. A crucial step towards it is being able to read traffic signs along the roads. Unfortunately, state-of-the-art traffic sign detectors currently ignore the existence of additional traffic signs. Yet being able to recognize these is a requirement for the task of automated driving and automated map data updates, because they further determine the meaning or validity of main signs. In this paper we aim at the detection of these additional signs, a first step towards their recognition. We will have a careful look at suitable evaluation measures and then use these to compare our proposed MSER-based approach to a selection of five differing types of detectors from the literature. We achieved a substantial improvement of the state of the art with 90% successful detections with full sign content detection on a challenging dataset, while significantly reducing the number of false positives. We will present our database, which contains high-resolution images of German traffic signs suitable for optical character recognition. We rely on hand-labelled main signs to emphasize the focus on additional sign detection. Our results were confirmed on a validation set containing European additional signs.