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The International Archives oj the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing 2008 
Figure 1. DLR 3K-Camera system consisting of three 
Canon EOS IDs Mark II, integrated in a ZEISS 
aerial camera mount 
Fig 2 illustrates the image acquisition geometry of the DLR 3K- 
camera system. Based on the use of 50 mm Canon lenses, the 
relation between airplane flight height, ground coverage, and 
pixel size is shown, e.g. the ground sampling distance (GSD) at 
a flight height of 1000 m is 15 cm in nadir (20 cm in side-look) 
and the image array covers up 2.8 km in width. 
2.2 Test Site and 3K Imagery 
The processing chain was tested on data obtained at the 
motorways A95 and A96 near Munich, and the “Mittlere Ring” 
in Munich. The “Mittlere Ring” is a circular main road and 
serves as the backbone for the city traffic in Munich. It and the 
adjacent Motorways A95 and A96 are used to full capacity 
regularly on weekdays during rush hour, and are quite 
populated all day long. Therefore, these roads are good 
candidates to find a broad spectrum of traffic situations ranging 
from free flowing traffic to traffic jam. Hence, they are good 
targets for aerial images obtained for testing traffic monitoring 
applications. However, data were taken on 30.04.2007 at noon, 
which was not during rush hour at all. Data acquisition was 
performed on two flight strips, one flying ENE, covering the 
A96 and the western part of the “Mittlere Ring”, the other one 
flying WSW. Thereby, the southern part of the “Mittlere Ring” 
and the motorway A95 were imaged. The flight height was 
1000 m above ground for both strips which leads to a GSD of 
15 cm in the nadir camera and up to 20 cm in the side-look 
cameras. After that, the flight track was repeated at a flight 
level of 2000 m above ground. 
For further analysis, 3K images were geocoded using onboard 
GPS/IMU measurements with an absolute position error of 3m 
in nadir images and around one pixel relative. The relative 
georeferencing error between successive images mainly 
influences the accuracy of the derived vehicle velocities. Based 
on simulations and real data, the accuracy of the measured 
velocity was around 5 km/h depending on the flight height 
(Hinz et al. 2007). 
Coverage 
Figure 2. Illustration of the image acquisition geometry. The tilt 
angle of the sideward looking cameras is approx. 
2.3 Road Database 
Data from a road database will be used as a priori information 
for the automatic detection of road area and vehicles. One of 
these road databases has been produced by the NAVTEQ 
Company. The roads are given by polygons which consist of 
piecewise linear “edges,” grouped as “lines” if the attributes of 
connected edges are identical. Up to 204 attributes are assigned 
to each polygon, including the driving direction on motorways, 
which is important for automated tracking. Recent validations 
of position accuracy of NAVTEQ road lines resulted in 5m 
accuracies for motorways. 
3. PROCESSING CHAIN 
On the data obtained as described before, the processing chain 
for traffic monitoring was tested. This experimental processing 
chain, consisting of several modules can be roughly divided 
into three major steps. These are road extraction, car detection, 
and car tracking (see also fig 4). 
3.1 Road Extraction 
For an effective real time traffic analysis, the road surface needs 
to be clearly determined. The road extraction starts by forming 
a buffer zone around the roads surfaces using a road database as 
described above as a basis for the buffer formation process. In 
the next step, two different methods for further feature analysis 
can be applied. Both modules automatically delineate the 
roadsides by two linear features. One module works as follows: 
Within the marked buffer zone, edge detection and feature 
extraction techniques are used. The critical step of edge 
detection is based on an edge detector proposed by Phillipe 
Paillau for noisy SAR images (Paillou, 1997). Derived from 
Deriche filter (Deriche, 1987) and proposed for noisy SAR 
images, we found this edge detector after ISEF filtering (Shen 
and Caston, 1992) extremely efficient for our purpose of 
finding edges along the roadsides and suppressing any other 
kind of surplus edges and noise present. With this method, 
mainly the edge between the tarry road area and the vegetation 
is found. The alternative module searches for the roadside 
markings by extracting lines on a dynamic threshold image. In 
this module, only the longest lines are kept representing the 
drawn through roadside marking lines. As a side effect, the 
dashed midline markings are detected in this module, too. These 
markings often cause confusion in the successional car 
detection, since they resemble white cars. However, these false 
alarms can be deleted from car detection,