Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2011 Article ID 164956 14 pages doi 2011 164956 Research Article A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts Vikas Reddy 1 2 Conrad Sanderson 1 2 and Brian C. Lovell1 2 1NICTA . Box 6020 St Lucia QLD 4067 Australia 2 School ofITEE The University of Queensland QLD 4072 Australia Correspondence should be addressed to Conrad Sanderson conradsand@ Received 27 April 2010 Revised 23 August 2010 Accepted 26 October 2010 Academic Editor Carlo Regazzoni Copyright 2011 Vikas Reddy et al. 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. 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
