Adaptive fusion for diurnal moving object detection

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 paper, we 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. The 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.