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How close are we to solving the problem of automated visual surveillance?

A review of real-world surveillance, scientific progress and evaluative mechanisms

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Abstract

The problem of automated visual surveillance has spawned a lively research area, with 2005 seeing three conferences or workshops and special issues of two major journals devoted to the topic. These alone are responsible for somewhere in the region of 240 papers and posters on automated visual surveillance before we begin to count those presented in more general fora. Many of these systems and algorithms perform one small sub-part of the surveillance task, such as motion detection. But even with low level image processing tasks it is often difficult to compare systems on the basis of published results alone. This review paper aims to answer the difficult question “How close are we to developing surveillance related systems which are really useful?” The first section of this paper considers the question of surveillance in the real world: installations, systems and practises. The main body of the paper then considers existing computer vision techniques with an emphasis on higher level processes such as behaviour modelling and event detection. We conclude with a review of the evaluative mechanisms that have grown from within the computer vision community in an attempt to provide some form of robust evaluation and cross-system comparability.

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  1. School of Computing, University of Leeds, Leeds, LS2 9JT, UK

    Hannah M. Dee

  2. Digital Imaging Research Centre, Kingston University, Kingston-upon-Thames, KT1 2EE, UK

    Sergio A. Velastin

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Dee, H.M., Velastin, S.A. How close are we to solving the problem of automated visual surveillance?. Machine Vision and Applications 19, 329–343 (2008). https://doi.org/10.1007/s00138-007-0077-z

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