AUTOMATED 3D ROAD SIGN MAPPING WITH STEREOVISION-BASED 
MOBILE MAPPING EXPLOITING DISPARITY INFORMATION FROM 
DENSE STEREO MATCHING 
S. Cavegn, S. Nebiker 
Institute of Geomatics Engineering 
FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland 
(stefan.cavegn, stephan.nebiker)@fhnw.ch 
Commission IV, WG IV/2 
KEY WORDS: Mobile, Mapping, Point Cloud, Extraction, Classification, Matching, Infrastructure, Inventory 
ABSTRACT: 
This paper presents algorithms and investigations on the automated detection, classification and mapping of road signs which 
systematically exploit depth information from stereo images. This approach was chosen due to recent progress in the development of 
stereo matching algorithms enabling the generation of accurate and dense depth maps. In comparison to mono imagery-based 
approaches, depth maps also allow 3D mapping of the objects. This is essential for efficient inventory and for future change 
detection purposes. Test measurements with the mobile mapping system by the Institute of Geomatics Engineering of the FHNW 
University of Applied Sciences and Arts Northwestern Switzerland demonstrated that the developed algorithms for the automated 3D 
road sign mapping perform well, even under difficult to poor lighting conditions. Approximately 90% of the relevant road signs with 
predominantly red, blue and yellow colors in Switzerland can be detected, and 85% can be classified correctly. Furthermore, fully 
automated mapping with a 3D accuracy of better than 10 cm is possible. 
1. INTRODUCTION 
A great many road signs can be found along the streets in 
Western Europe, e.g. in Switzerland alone approximately five 
millions signs are in existence. In many cases, there is no digital 
information available concerning position and state of these 
road signs and several of them are believed to be unnecessary. 
For analysis purposes and to overcome these issues, a road sign 
inventory could be the solution. To establish such an inventory, 
attribute data and images of the road signs are traditionally 
captured in situ and the position is determined using a GNSS 
receiver with meter to decimeter accuracy. In recent years, 
mapping and inventory has increasingly been carried out on 
basis of data recorded with mobile mapping systems as they 
permit efficient mapping of 3D road infrastructure assets 
without disrupting the traffic flow and endangering the 
surveying staff. In Belgium, road signs over the whole country 
could be mapped by means of laserscanning data; attribute data 
was mostly obtained by user interaction (Trimble 2009). In The 
Netherlands, road sign mapping was carried out manually on 
basis of panorama imagery which was collected every 5 m (de 
With et al. 2010). If road signs can largely be extracted 
automatically from georeferenced images, the manual effort can 
be reduced significantly. This paper introduces algorithms for 
the automated road sign detection and classification from 
mobile stereo image sequences as well as the determination of 
the 3D position and other attribute data. These algorithms were 
primarily optimized for road signs in Switzerland with mainly 
red, blue and yellow colors which can appear in the shapes 
circle, triangle, rectangle, square and diamond as well as in four 
different dimensions depending on the road type (see Figure 1). 
However, the algorithms can be adapted to road signs of other 
58 
countries. Since driver assistance systems or intelligent 
autonomous vehicles are not the focus of this work, real-time 
execution is not of top priority. Instead, the emphasis is on 
completeness, correctness and geometric accuracy. 
  
  
    
  
  
  
  
  
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Figure 1. Road signs in Switzerland which can automatically be 
detected, classified and mapped with the developed algorithms 
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