| Adaptive Fusion for
Diurnal Moving Object Detection
Presented by: Sohail Nadimi
Fusion of different sensor types (e.g. video, thermal infrared)
and sensor selection strategy at signal or pixel level is
a non-trivial task that requires a well-defined structure.
In this talk, I provide a novel fusion architecture that is
flexible and can be adapted to different types of sensors.
The new fusion architecture provides an elegant approach to
integrating different sensing phenomenology, sensor readings,
and contextual information. A cooperative coevolutionary method
is introduced for optimally selecting fusion strategies. We
provide results in the context of a moving object detection
system for a full 24 hours diurnal cycle in an outdoor environment.
Our results indicate that our architecture is robust to adverse
illumination conditions and the evolutionary paradigm can
provide an adaptable and flexible method for combining signals
of different modality.