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Multidimensional Systems and Signal Processing

, Volume 30, Issue 1, pp 311–342 | Cite as

Anomaly detection with a moving camera using multiscale video analysis

  • Gustavo H. F. de Carvalho
  • Lucas A. ThomazEmail author
  • Allan F. da Silva
  • Eduardo A. B. da Silva
  • Sergio L. Netto
Article
  • 140 Downloads

Abstract

This paper addresses the problem of abandoned object detection in a cluttered environment using a camera moving along a straight track. The developed system compares captured images to a previously recorded reference video, thus requiring proper temporal alignment and geometric registration between the two signals. A real-time constraint is imposed onto the system to allow an effective surveillance capability in practical situations. In this paper, we propose to deal with the simultaneous detection of objects of different sizes using a multiresolution approach together with normalized cross-correlation and a voting step. In order to develop and properly assess the proposed method we designed a database recorded in a real surveillance scenario, consisting of an industrial plant containing a large number of pipes and rotating machines. Also, we have devised a systematic parameter tuning routine that allows the system to be adapted to different scenarios. We have validated it using the designed database. The obtained results are quite effective, achieving real-time, robust abandoned object detection in an industrial plant scenario.

Keywords

Video surveillance Moving camera Abandoned object detection Cluttered environment 

Notes

Acknowledgements

This work was developed with the partial support of Statoil Brazil, Petrobras, ANP, and CAPES and CNPq funding agencies.

Funding

Funding was provided by Conselho Nacional de Desenvolvimento Científico e Tecnológico (Grant No. 203876/2017-2), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Grant No. 88881.135449/2016-01), Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Electrical Engineering ProgramFederal University of Rio de JaneiroRio de JaneiroBrazil

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