电子与通信工程专业英语Unit 18 Integrating RFID on Event-based Hemispheric Imaging for Internet of Things.pptVIP

　　1．Introduction　　The remote activity monitoring across large environments, such as government facilities, public buildings or industrial environments in real time, is a prerequisite for various monitoring applications. Such activities can also prove a useful addition in assistive environments in smaller size spaces, like houses and offices. Modern video-based surveillance systems, which employ powerful real time analysis techniques, are widely deployed. Additionally, the use of multiple cameras to provide surveillance coverage over a wider area while ensuring object visibility over a large range of depths, introduces the need to coordinate the cameras in order to detect events of interest, something which increases system complexity. 　　Radio Frequency Identification (RFID) technology is arguably the ideal solution for object identification. It has successfully been used in a large variety of applications, like enterprise supply chain management for inventorying, tracking and of course objects identification. RFID may also prove useful for pervasive computing, for providing identity to virtually everything. It is not accidental that RFID, along with wireless sensor and nanotechnologies have been combined to form what is known as the Internet of Things. 　　For nearly every pervasive computing application, another vital requirement is real time locating, which emanates from the inherent need for just-in-time actionable information. Over the years, many systems have addressed the problem of automatic location sensing with various techniques. In this paper we elaborate on the feasibility of developing a hybrid system by combining video surveillance feed and RFID, in order to provide a solid system for automatic identification and tracking of objects in the output of a video camera. The use of hemispheric imaging cameras that maximize the area coverage of a surveillance system is examined, in order to eliminate the need for multiple cameras and the factors that infl