Road safety is influenced by the adequate placement of traffic signs. As the visibility of road signs degrades over time due to e.g. aging, vandalism or vegetation coverage, sign maintenance is required to preserve a high road safety. This is commonly performed based on inventories of traffic signs, which should be conducted periodically, as road situations may change and the visibility of signs degrades over time. These inventories are created efficiently from street-level images by (semi-)automatic road sign recognition systems, employing computer vision techniques for sign detection and classification. Instead of periodically repeating the complete surveying process, these automated sign recognition systems enable re-identification of the previously found signs. This results in the highlighting of changed situations, enabling specific manual validation of these cases. This paper presents a mutation detection approach for semi-automatic updating of traffic sign inventories, together with a case study to assess the practical usability of such an approach. Our system re-identifies 94.8% of the unchanged signs, thereby resulting in a significant reduction of the manual effort required for the semi-automated actualization of the inventory. As the amount of changes equals to 16:9% of the already existing signs, this study also clearly shows the economic relevance and usefulness of periodic updating road sign surveys.
|Title of host publication||Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP 2014), 5-8 January 2014, Lisbon, Portugal|
|Place of Publication||Lisbon, Portugal|
|Publication status||Published - 2014|