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1. INTRODUCTION 
Today real-time mapping concentrates on two to- 
pics: the first topic addresses the combination of 
data collection sensors like positioning and ima- 
ging sensors for digital mapping. The second to- 
pic is concerned with the development of mapping 
procedures, i.e., methods and algorithms for the 
identification and location of objects which have 
to be captured and stored in a database. Because 
of the real-time aspect of mapping simple but re- 
liable procedures are required. With regard to 
this second topic practice looks quite different. A 
mapping equipment enables the user to identify 
and locate objects in an absolute reference frame. 
The automatic identification of objects is still a 
hard problem of vision research. 
Real-time classification procedures are most suc- 
cessful in very restricted industrial scenes. In this 
case the number of object classes is very small. 
By thresholding the images the objects are sepa- 
rated from the background. The extraction of 
some simple features often is sufficient for the 
identification of the objects. Significantly more 
demanding are outdoor scenes, where in general 
a lot of objects are present and the scene is com- 
plex. 
This paper focuses on the identification of sim- 
ple objects in outdoor scenes. As an example we 
use traffic signs and assume that the objects of 
interest are located in a more or less natural en- 
vironment. Such a scene might be captured by 
a Highway Inventory System (Schwarz, 1992) or 
similar surveying vans (Novak, 1990). For the ex- 
periments in this paper we use a short sequence of 
colour images. The images are taken by a stan- 
dard video camera, i.e., the full frames (25 Hz) 
are composed by the odd and even fields of the 
half frames. Thus a full frame is the smallest mo- 
tion unit in which information about the scene is 
captured and, in addition, the effects of the mo- 
vement of the car are represented. 
The geometric structure or shape of the objects 
of interest is simple. They can be modelled by a 
plane which is spatially limited, for example, by 
triangular, rectangular or circular border lines. 
Traffic signs are typical objects of this class. For 
the recognition the colour of those objects can be 
expected to be an important clue. The use of a 
small number of different colours like red, yellow, 
blue, black and white keeps the discrimination 
between objects relatively easy. 
In developing a procedure for the identification 
of objects from an image sequence some specific 
problems have to be taken into account. In gene- 
ral, the size of an object in the image is unknown. 
The projection of the object leads to a perspective 
distortion of the border line. Thirdly the move- 
ment of the video camera during the exposure is 
the reason for motion blur. 
An important point for solving the mapping task 
by an automatic procedure is the efficient use of 
different information sources like the colour, mo- 
tion and contour of the object. Simple algorithms 
are required to achieve real-time capabilities. The 
procedure we propose for the recognition of sim- 
ple objects consists of the following steps. First 
regions of interest, i.e. regions which may con- 
tain the unknown objects, have to be detected. 
Although in principle colour, motion and contour 
may contribute to solve this task only motion is 
used in this step. The result of the motion seg- 
mentation is a displacement vector or displace- 
ment field of this region. The displacement field 
can be used to restore the image with the aim 
of eliminating the effect of motion blur. The re- 
stored image is well prepared for the extraction of 
the border line of the object by which the location 
of the object in the image is determined. Because 
of the 3D to 2D projection of the object its bor- 
der line is distorted. In consequence, for recogni- 
tion it is advantageous to extract affine-invariant 
features from this contour. The last step of the 
procedure is a maximum likelihood classification. 
The affine-invariant features can be used to deter- 
mine the most likely object class for the unknown 
localized object. In the following these main steps 
of the analysis are discussed further. Concerning 
the interpretation the suitability and the separa- 
bility of the object features is of special interest. 
Therefore, this aspect will be analysed in more 
detail. 
2. DETERMINATION OF THE 
REGIONS OF INTEREST 
A first step in the recognition of an object is to 
identify a region in the image in which the sear- 
ched object is supposed to appear. This region 
is called the region of interest. In the case of 
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