1 
1 \ b ] 
I A) ♦ 
. ■ ? 
* 
iv, 2a 
ft. 
J- » • 
f 
,0 s 
ul 
In this paper we presented a car detecting and a tracking algo 
rithm which have been especially chosen and adapted to the given 
situation, the flying traffic monitor ARGOS. It was shown how 
they work and that they brought satisfying results depending on 
the environmental conditions. Furthermore it was shown, where 
the approach has problems and continuous work can be done. 
Surely the system can be improved in some points and a few of 
them should be given here. First of all the street accuracy prob 
lem which could be easily solved by using another database. And 
it should be mentioned that there was already the attempt to use 
the more accurate street database Atkis. On the one hand the co 
ordinates were indeed more exact and yielded slightly better de 
tection rates, but on the other hand the database divides the street 
network into too small segments, which take a lot more time to 
process one by one. Additionally the achieved data should be 
mapped on Navteq segments, which would not be easy. So the 
next step is to integrate the newest version of the Navteq database 
being bought at the time. 
Furthermore the edge detection could be optimized for example 
by running it on the GPU, but it has not been considered so far. 
Another idea is to compute the filtering in the frequency space. 
The Fourier-transformed images and filters just have to be multi 
plied in frequency space and transformed back. The only prob 
lem is that the filters change with every street segment, so there 
are four filters and four filtered images to be transformed ev 
ery time. The approach was already explored, but the Fourier- 
transformation implemented in OpenCV needs longer than direct 
convolution, because it uses floating point numbers. 
Next to this the detected cars could be verified by a more ex 
pensive algorithm like a Bayesian Network or a Support Vector 
Machine because some of the false positives do not look like a 
car at all. So they would be easy to discard. 
ACKNOWLEDGEMENTS 
The authors would like to thank Dr. Franz Kurz and Dr. Do 
minik Rosenbaum (Remote Sensing Technology Institute, Ger 
man Aerospace Center, Oberpfaffenhofen) for providing the Geo- 
TIFF images and navigation data. 
REFERENCES 
Figure 11 : Tracked objects filtered by CC-threshold 0.9 ( 100% 
correct) 
Ernst, I., Hetscher, M., Thiessenhusen, K., Ruhe, M., Bomer, A. 
and Zuev, S., 2005. New approaches for real time traffic acqui 
sition with airbone systems. Int. Archives of Photogrammetry, 
Remote Science and Spatial Information Sciences 36, pp. 68-73. 
Grabner, H., Nguyen, T. T., Gruber, B. and Bischof, H., 2008. 
On-line boosting-based car detection fron aerial images. ISPRS 
Journal of Photogrammetry and Remote Sensing. 
Haag, M. and Nagel, H., 1999. Combination of edge element 
and optical flow estimates for 3d-model-based vehicle tracking 
in traffic image sequences. International Journal of Computer 
Vision 35, pp. 295-319. 
Hinz, S., 2004. Detection of vehicles and vehicle queues in high 
resolution aerial images. Photogrammetrie - Femerkundung - 
Geoinformation (PFG) 3/04, pp. 201 - 213. 
179