| Robust Probabilistic Cue Integration for Multiple Cameras Up to now, it is an unsolved problem, how sensor data selection
and fusion shall be done in the case that multiple cameras and
multiple cues from each of the cameras are available.
Thus, the goal of the project is the development of a probabilistic
cue integration mechanism for object tracking using multiple cameras.
The basis is given by the approach on cue integration
by Democratic Integration (DI) and
probabilistic methods for state estimation,
and optimal sensor data selection. The key idea is to first integrate
the DI priciple in a probabilistic framework. Then, the method is extented from
single sensors to multiple sensors by exploiting techniques from
projective geometry. As a result one gets global fusion maps from
the local maps of the different sensors. The approach is applied and
verified in real-time object tracking using multiple cameras in
two selected applications: surveillance tasks and moble robot
navigation.
| Project manager:
Prof. Dr.-Ing. Joachim Denzler
Project participants:
Dipl.-Inf. Matthias Zobel
Duration: 1.1.2002 - 31.12.2002
Sponsored by:
BaCaTeC
Mitwirkende Institutionen:
Cognitive Science Department, University of California, San Diego
Contact:
Denzler, Joachim
E-Mail: denzler@informatik.uni-jena.de
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