Abstract

Several applications require information about street furniture. Part of the task is to survey all traffic signs. This has to be done for millions of km of road, and the exer-cise needs to be repeated every so often. A van with 8 roof-mounted cameras drove through the streets and took images every meter. The paper proposes a pipeline for the efficient detection and recognition of traffic signs. The task is chal-lenging, as illumination conditions change regularly, occlu-sions are frequent, 3D positions and orientations vary sub-stantially, and the actual signs are far less similar among equal types than one might expect. We combine 2D and 3D techniques to improve results beyond the state-of-the-art, which is still very much preoccupied with single view analysis. 1.