Abstract-The application of intelligent systems composed by smart cameras is continuously spreading in a wide range of applications, playing a key role in public, military and commercial scenarios. As well, in the last years, the capability of Wireless Sensor Networks to collect information from the environment in a distributed manner has been successfully applied in both civilian and military applications. In this paper, basing on recent studies on autonomous cognitive systems, we explore the concepts for designing interactive, adaptable and intelligent multi-sensor surveillance systems able to react to situations in a preventive way by using actuators placed in the monitored environment. To this end, taking inspiration from Ambient Intelligence (AmI) and Cognitive Radio (CR) paradigms, fusion of information provided by heterogeneous sensors is used to improve awareness regarding surrounding environment.

Abstract.In the last few years, the application of ICT technologies in automotive field has taken an increasing role in improving both the safety and the driving comfort. In this context, systems capable of determining the traffic situation and/or driver behavior through the analysis of signals from multiple sensors (e.g. radar, cameras, etc...) are the subject of active research in both industrial and academic sectors. The extraction of contextual information through the analysis of video streams captured by cameras can therefore have implications in many applications focused both on prevention of incidents and on provision of useful information to drivers. In this paper, we investigate the study and implementation of algorithms for the extraction of context data from on-board cameras mounted on vehicles. A camera is oriented so as to frame the portion of road in front of the vehicle while the other one is positioned inside the vehicle and pointed on the driver.

Abstract-In the last decade, the design of smart tools applied to the automotive sector is becoming more and more required to support driving tasks and to increase drivers' safety. In this work, a system for the joint analysis of the on board/off board vehicle context is proposed to derive considerations on the driver's behavior and to detect possible dangerous situations. In particular, a cognitive model is exploited to provide a representation of interactions among the driver, the vehicle and the surrounding environment. Preliminary studies and results are presented concerning such a cognitive approach and the representation of occurring events for situation assessment and management towards maintenance and increasing of driver's safety.

Critical Infrastructure Protection (CIP) represents a particularly relevant issue worldwide. Moreover, the evolution of technologies at the disposal of criminal activities is leading to an increasing need for a technological basis and relevant knowledge to improve security capabilities. Especially, more work has to be done in defining a security architecture that takes into account cascading effects, inter and intra dependencies, distributes the intelligence in the infrastructure to increase its robustness, trustworthiness and resilience. According to this statement, an innovative situation awareness and assessment based approach for the monitoring of Critical Infrastructures is proposed. In particular, based on recent studies on artificial cognitive systems, we explore the concepts for designing interactive, adaptable and intelligent multi sensor surveillance systems able to react to situations in a preventive and manageable way. To reach this goal, in this paper we emphasize that innovative strategies could be based on the integration of heterogeneous sensing devices, such as distributed cognitive radio sensors for communication and monitoring and a network of smart cameras for the interpretation of interactions and events.

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