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This analysis of visual cues in multi camera networks enables a wide range of applications ranging from smart home and office automation to large area surveillance and traffic surveillance. We focus on sparse camera networks in dense camera networks, most of which have large areas of overlapping cameras in good research horizon. A sparse camera network uses a large area to monitor as few cameras as possible, and most cameras do not overlap each other’s field of view. This task challenges the lack of knowledge about topological networks, the specific changes in appearance and motion, the tracking of different opinions of the target, and the difficulty of understanding complex events in the network. In this review, we give a comprehensive overview of recent research efforts to address topology learning, object appearance modeling, and global activity understanding of sparse camera network problems. In addition, some open research issues are discussed at the present time.

RETRACTED ARTICLE: Camera network analysis for visual surveillance in industrial electronic context