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Communication Dans Un Congrès Année : 2015

OR-PCA with MRF for Robust Foreground Detection in Highly Dynamic Backgrounds

Résumé

Accurate and efficient foreground detection is an important task in video surveillance system. The task becomes more critical when the background scene shows more variations, such as water surface, waving trees, varying illumination conditions, etc. Recently, Robust Principal Components Analysis (RPCA) shows a very nice framework for moving object detection. The background sequence is modeled by a low-dimensional subspace called low-rank matrix and sparse error constitutes the foreground objects. But RPCA presents the limitations of computational complexity and memory storage due to batch optimization methods, as a result it is difficult to apply for real-time system. To handle these challenges, this paper presents a robust foreground detection algorithm via Online Robust PCA (OR-PCA) using image decomposition along with continuous constraint such as Markov Random Field (MRF). OR-PCA with good initialization scheme using image decomposition approach improves the accuracy of foreground detection and the computation time as well. Moreover, solving MRF with graph-cuts exploits structural information using spatial neighborhood system and similarities to further improve the foreground segmentation in highly dynamic backgrounds. Experimental results on challenging datasets such as Wallflower, I2R, BMC 2012 and Change Detection 2014 dataset demonstrate that our proposed scheme significantly outperforms the state of the art approaches and works effectively on a wide range of complex background scenes.
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Dates et versions

hal-01374213 , version 1 (30-09-2016)

Identifiants

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Sajid Javed, Seon Ho Oh, Andrews Sobral, Thierry Bouwmans, Soon Ki Jung. OR-PCA with MRF for Robust Foreground Detection in Highly Dynamic Backgrounds. Asian Conference on Computer Vision, ACCV 2014, Nov 2015, Singapore, Singapore. pp.284 - 299, ⟨10.1007/978-3-319-16811-1_19⟩. ⟨hal-01374213⟩

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