Authors:
Lykele Hazelhoff
1
;
Ron op het Veld
2
;
Ivo Creusen
1
and
Peter H. N. de With
1
Affiliations:
1
CycloMedia Technology B.V and Eindhoven University of Technology, Netherlands
;
2
Eindhoven University of Technology, Netherlands
Keyword(s):
Object Detection, Traffic Sign Recognition, Object Classification.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Mobile Imaging
Abstract:
Road safety is influenced by the accurate placement and visibility of road signs, which are maintained based on
inventories of traffic signs. These inventories are created (semi-)automatically from street-level images, based
on object detection and classification. These systems often neglect the present complimentary signs (subsigns),
although clearly important for the meaning and validity of signs. This paper presents a generic, learning-based
approach for both detection and classification of subsigns, which is based on the same principles as the system
employed for finding traffic signs and can be used as an extension to automated inventory systems. The
system starts with detection of subsigns in a region below each detected sign, followed by analysis of the
results obtained for all capturings of the same sign. When a subsign is found, the corresponding pixel regions
are extracted and subject to classification. This recognition system is evaluated on 3;104 signs (397 with
subsign)
identified by an existing inventory system. At a detection rate of 98%, only 757 signs (24:4% of the
signs) are labeled as containing a subsign, while 91:4% of the subsigns of a class known to our classifier are
also classified correctly.
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