Sparse camera network for visual surveillance – a comprehensive survey

Song, M. and Tao, D. and Maybank, Stephen J. (2013) Sparse camera network for visual surveillance – a comprehensive survey. CoRR ,

Full text not available from this repository.

Official URL: http://arxiv.org/abs/1302.0446

Abstract

Technological advances in sensor manufacture, communication, and computing are stimulating the development of new applications that are transforming traditional vision systems into pervasive intelligent camera networks. The analysis of visual cues in multi-camera networks enables a wide range of applications, from smart home and office automation to large area surveillance and traffic surveillance. While dense camera networks - in which most cameras have large overlapping fields of view - are well studied, we are mainly concerned with sparse camera networks. A sparse camera network undertakes large area surveillance using as few cameras as possible, and most cameras have non-overlapping fields of view with one another. The task is challenging due to the lack of knowledge about the topological structure of the network, variations in the appearance and motion of specific tracking targets in different views, and the difficulties of understanding composite events in the network. In this review paper, we present a comprehensive survey of recent research results to address the problems of intra-camera tracking, topological structure learning, target appearance modeling, and global activity understanding in sparse camera networks. A number of current open research issues are discussed.

Metadata

Item Type:	Article
Keyword(s) / Subject(s):	sparse camera network, visual surveillance
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Sarah Hall
Date Deposited:	25 Jul 2013 09:56
Last Modified:	09 Aug 2023 12:33
URI:	https://eprints.bbk.ac.uk/id/eprint/7774

Statistics

Archive Staff Only (login required)

Edit/View Item