Authors:
Lykele Hazelhoff
;
Ivo Creusen
and
Peter H. N. de With
Affiliation:
CycloMedia Technology B.V. and Eindhoven University of Technology, Netherlands
Keyword(s):
Object Detection, Traffic Sign Recognition, Object Classification, Mutation Detection.
Abstract:
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,
togethe
r 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.
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