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  • Research Article
  • Open Access

A Review and Comparison of Measures for Automatic Video Surveillance Systems

  • Axel Baumann1,
  • Marco Boltz1,
  • Julia Ebling1Email author,
  • Matthias Koenig1,
  • HartmutS Loos1,
  • Marcel Merkel1,
  • Wolfgang Niem1,
  • JanKarl Warzelhan1 and
  • Jie Yu1
EURASIP Journal on Image and Video Processing20082008:824726

Received: 30 October 2007

Accepted: 12 June 2008

Published: 8 July 2008


Today's video surveillance systems are increasingly equipped with video content analysis for a great variety of applications. However, reliability and robustness of video content analysis algorithms remain an issue. They have to be measured against ground truth data in order to quantify the performance and advancements of new algorithms. Therefore, a variety of measures have been proposed in the literature, but there has neither been a systematic overview nor an evaluation of measures for specific video analysis tasks yet. This paper provides a systematic review of measures and compares their effectiveness for specific aspects, such as segmentation, tracking, and event detection. Focus is drawn on details like normalization issues, robustness, and representativeness. A software framework is introduced for continuously evaluating and documenting the performance of video surveillance systems. Based on many years of experience, a new set of representative measures is proposed as a fundamental part of an evaluation framework.


Ground TruthAnalysis TaskNormalization IssueVideo AnalysisEvaluation Framework

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Authors’ Affiliations

Corporate Research, Robert Bosch GmbH, Stuttgart, Germany


© Axel Baumann et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.