Sparse Camera Network for Visual Surveillance -- A Comprehensive Survey

TL;DR

This survey reviews recent advances in sparse camera networks for visual surveillance, focusing on challenges like tracking, topology learning, and activity understanding in large, non-overlapping camera setups.

Contribution

It provides a comprehensive overview of recent research on sparse camera networks, highlighting key problems and open issues in the field.

Findings

Analysis of intra-camera tracking techniques

Methods for learning network topology

Approaches to global activity understanding

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.