A low-complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts

Reddy, Vikas, Sanderson, Conrad and Lovell, Brian C. (2011) A low-complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts. EURASIP Journal on Image and Video Processing, 2011 164956: 1-14.


Author Reddy, Vikas
Sanderson, Conrad
Lovell, Brian C.
Title A low-complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts
Journal name EURASIP Journal on Image and Video Processing   Check publisher's open access policy
ISSN 1687-5176
1687-5281
Publication date 2011
Sub-type Article (original research)
DOI 10.1155/2011/164956
Volume 2011
Issue 164956
Start page 1
End page 14
Total pages 14
Editor Carlo Regazzoni
Place of publication Heidelberg, Germany
Publisher SpringerOpen
Collection year 2012
Language eng
Formatted abstract For the purposes of foreground estimation, the true background model is unavailable in many practical circumstances and needs to be estimated from cluttered image sequences. We propose a sequential technique for static background estimation in such conditions, with low computational and memory requirements. Image sequences are analysed on a block-by-block basis. For each block location a representative set is maintained which contains distinct blocks obtained along its temporal line. The background estimation is carried out in a Markov Random Field framework, where the optimal labelling solution is computed using iterated conditional modes. The clique potentials are computed based on the combined frequency response of the candidate block and its neighbourhood. It is assumed that the most appropriate block results in the smoothest response, indirectly enforcing the spatial continuity of structures within a scene. Experiments on real-life surveillance videos demonstrate that the proposed method obtains considerably better background estimates (both qualitatively and quantitatively) than median filtering and the recently proposed “intervals of stable intensity” method. Further experiments on the Wallflower dataset suggest that the combination of the proposed method with a foreground segmentation algorithm results in improved foreground segmentation.
Keyword Statistical-analysis
Visual surveillance
Moving-objects
Robust
Subtraction
Systems
Video
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Information Technology and Electrical Engineering Publications
 
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Created: Sun, 20 Feb 2011, 00:04:52 EST