Co-FRIEND aims to design a framework for understanding human activities in real environments, through an artificial cognitive vision system, identifying objects and events, and extracting sense from scene observation. It will manage uncertainty and change, and will create analysis meaning. A heterogeneous sensor network (wide angle and PTZ cameras in airport immediate area, and GPS wide area vehicle monitoring) will be deployed on Toulouse AIRPORT by SILOGIC and READING. The cognitive capabilities developed by INRIA will be demonstrated by monitoring outdoor airport activities. Deficits of current approaches to scene understanding will be addressed by CSL and LEEDS through machine learning, requiring explicit domain modelling. This challenge will provide generic research insights and demonstrate the impact of our conceptual and technical innovations.
We will improve the performance and integration of relevant cognitive functions: learning, dynamic context adaptation, perception, tracking, and recognition. Integrating them in Co-FRIEND will address the creation and exploitation of knowledge by. Our innovations will provide relevant and flexible learning and reasoning capabilities that adapt in a largely unsupervised way to change and new events. Feedback and multi-data fusion will be exploited to achieve robust detection and efficient tracking of objects in real and complex scenes.
With dynamic understanding contextualized to a learnt domain, porting and exploiting the technology in other installations, new contexts or other domains will be demonstrated. Research in uncertainty management will focus on maintaining long-term coherence in variable and complex scene environment. Cognitive and active visions will highlight human activity understanding by interpreting gestures and wide area monitoring. Co-FRIEND will evaluate and illustrate that robustness and adaptability in crowded environments can be obtained by multi-modal sensor fusion in a cognitive architecture.