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grid vs map fusion mode times 
Figure 10: ATU and SDF execution times for both modes 
7. CONCLUSIONS 
This paper presented an automated solution for visual traffic 
monitoring based on a network of distributed tracking units. 
The system can be easily adjusted and parameterised in order to 
be used in several traffic monitoring applications, as it was built 
based on results acquired from two diverse pilot installations. 
The first prototype, installed at an airport APRON, was using 
an outdoor scene with large field of view while the second 
prototype, installed in a highway tunnel, was using an indoors 
scene with smaller distances and more occlusions. The results 
presented were focused on the two most important modules of 
the system, the background extraction method and the data 
fusion technique. After both qualitative and quantitative 
evaluation of multiple alternatives, the non-parametric 
modelling method was chosen as the best solution for the 
system, regarding the background extraction module. On the 
other hand, both the data fusion techniques tested showed 
satisfying behaviour under different situations and the final 
choice between the two should depend on the specific 
application demands and infrastructure. An interesting future 
extension is to take advantage of the low bandwidth output of 
the SDF server in order to create a 3D synthetic representation 
of the scene under surveillance, which could be rendered at 
remote 3D displays. 
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ACKNOWLEDGEMETS 
This work was supported by the General Secretariat of Research 
and Technology Hellas under the InfoSoc “TRAVIS: Traffic 
VISual monitoring” project and the EC under the FP6 1ST 
Network of Excellence: “3DTV-Integrated Three-Dimensional 
Television - Capture, Transmission, and Display” (contract 
FP6-511568).