Domain-specific adaptations for region proposals

In this work we propose a novel approach towards the detection of all traffic sign boards. We propose to employ state-of-the-art region proposals as the first step to reduce the initial search space and provide a way to use a strong classifier for a fine-grade classification. We evaluate multiple region proposals on the domain of traffic sign detection and further propose various domain-specific adaptations to improve their performance. We show that edgeboxes with domain-specific learning and re-scoring based on trained shape information are able to significantly outperform remaining methods on German Traffic Sign Database. Furthermore, we show they achieve higher rate of recall with high-quality regions at the lower number of regions than the remaining methods.