the vast majority of road signs. The group of relevant road signs 
did also not include road signs for cross-roads which were not 
facing the mapped roads. The designed algorithms were 
performed in the distance range from 4 to 14 m. The first test 
with winter imagery yielded an automatic detection of 89% of 
all relevant road signs and a correct automatic classification of 
82% (see Table 1). Based on the summer imagery, 91% of all 
road signs could automatically be detected, and 89% could be 
classified correctly. With an additional user-supported step, the 
classification accuracy could be increased by another 5%. Due 
to this user-supported approach and some further built-in 
constraints, there were hardly any false positives. 
There are different reasons for an incorrect detection or 
classification of road signs. The detection process yields many 
red segments close to construction areas due to safety fences 
and warning devices. If the areas of these color segments are not 
too big, they can lead to false positives. Although the road signs 
in Switzerland generally appear in good condition, a few of 
them are yellowed. Thus, there are very low values for the 
saturation component. The same is also the case for road signs 
which are located in shadows. Since the defined threshold for 
this component cannot be exceeded, the detection of such road 
signs is not possible. In addition, there are some difficulties to 
automatically detect road signs if the depth maps are poor or 
incomplete. For several road signs, there exists no predefined 
template which leads to no or a wrong classification. A 
suboptimal threshold for the search image binarization can 
cause a too low correlation coefficient. 
  
  
  
  
  
  
  
  
Quan- | Detec- | Classi- False 
tity tion fication | Positives 
Winter all 152 55% 47% 2 
relevant 65 89% 82% 
summer all 96 71% 64% 4 
relevant 46 91% 89% 
  
  
  
  
  
Table 1. Detection and classification quality of the developed 
algorithms for two test campaigns 
For the evaluation of the geometric accuracy, 3D positions for 
22 reference road signs were determined using precise 
tachymetric observations. For the first test campaign, the 
differences between the 3D positions which were automatically 
derived by the described algorithms and the reference positions 
were computed. The maximal residual for a component is 16 
cm; however, most differences are in the range of 5 cm (see 
Table 2). For the empirical standard deviation of the 3D 
position difference, a value of 9.5 cm was calculated. 
  
  
  
  
  
  
in mm Aacross Aalong Aheight A3D 
Mean -36 23 -36 86 
Maximum 152 146 157 159 
Maifr 46 64 53 95 
  
  
  
  
  
  
Table 2. Mapping accuracy of the developed algorithms for a 
winter test campaign 
5. CONCLUSIONS AND OUTLOOK 
The investigations demonstrate the potential, in terms of 
automation and accuracy, offered by stereovision-based mobile 
mapping, if dense depth information is exploited. 
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. By means of a 
user-supported approach (Cavegn & Nebiker 2012), these rates 
can be increased by another 5%. Therefore, only 5 to 10% of 
the road signs have to be digitized either interactively in the 
stereo imagery or on site. Moreover, due to various constraints 
built into the algorithms, there are hardly any false positives. 
The presented approach is robust in terms of scaling, 
translations and small rotations. Although it is expected to 
obtain better results with nearby road signs, they can be 
detected in the whole predefined distance range interval. Road 
signs can arbitrarily be positioned in the image and small 
rotations are tolerated. Furthermore, it is possible to detect 
multiple road signs in the same image appearing in the shapes 
circle, rectangle, square, triangle and diamond. 
Not only depth maps of good quality but also sufficient color 
segmentation is crucial for the detection success. For this 
purpose, appropriate thresholds have to be applied. For the 
presented investigations, the interval for each component was 
chosen to be quite large. However, this was only possible since 
the search space could significantly be reduced due to depth 
information and false positives could be rejected using certain 
built-in constraints. 
Since a detection and classification quality of 100% is unlikely, 
it is possible to overlay the automatically mapped road signs in 
a georeferenced 3D video. The 3D videos can be viewed with a 
stereovision client (e.g. Burkhard et al. 2011), the results 
visually verified and the missing road signs quickly digitized. In 
the future, a first implementation of the algorithms for white 
and gray road signs which uses the depth information in 
combination with the Hough transform (Cavegn & Nebiker 
2012) will further be improved. The detection of other complex 
road signs and the identification of text (Wu et al. 2005) are 
also planned. An increase of the geometric accuracy and 
reliability could be achieved by matching in stereo image 
sequences (Huber et al. 2011). Tracking of road signs over 
multiple stereo image pairs would particularly effect an 
enhancement of the semantic quality. 
The goal of related work in progress is to determine the impact 
of different camera resolutions on the detection and 
classification quality. First investigations with a stereo system 
composed of industry cameras with a higher resolution of 
eleven megapixels show a slight improvement of the results. For 
the identification of text, the higher geometric resolution is 
mandatory. Current investigations also show that the depth map 
quality can significantly be increased using both image sensors 
with a higher resolution and adequate radiometric adjustments, 
which again positively affect the automated road sign mapping. 
62 
co rm M m] MJ "M CV m L1 Mmm 
Am N m 
MN eA S