Camera network analysis for visual surveillance in electric industrial context

Society is rapidly accepting the use of a wide variety of cameras location and applications: site traffic monitoring, parking lot surveillance, car and smart space. The camera provides data every day in an analysis by an effective way. Recent advances in sensor technology manufacturing, communications and computing are stimulating. The development of new applications that can change the traditional vision system incorporating universal smart camera network was processed. This analysis of visual cues in multi camera networks makes wide applications ranging from smart home and office automation to large area surveillance and traffic surveillance. And dense Camera networks, most of which have large overlapping areas of cameras. In the view of good research, we focus on sparse camera networks. One sparse camera network using large area surveillance was developed. As few cameras as possible, most cameras do not overlap each other's field of vision. This task is challenging. Lack of knowledge of topology network, the specific changes in appearance and movement track different opinions of the target, as well as difficulties understanding complex events in a network were observed. In this review, we present a comprehensive survey of recent studies. Results to solve the problem of topology learning, object appearance modeling and global activity understanding sparse camera network were determined. In addition, some of the current open research issues are discussed.