Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIP.2013.6738210
Title: Adaptive low rank and sparse decomposition of video using compressive sensing
Authors: Yang, F.
Jiang, H.
Shen, Z. 
Deng, W.
Metaxas, D.
Keywords: background subtraction
Compressive sensing
low rank and sparse decomposition
Issue Date: 2013
Citation: Yang, F.,Jiang, H.,Shen, Z.,Deng, W.,Metaxas, D. (2013). Adaptive low rank and sparse decomposition of video using compressive sensing. 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings : 1016-1020. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIP.2013.6738210
Abstract: We address the problem of reconstructing and analyzing surveillance videos using compressive sensing. We develop a new method that performs video reconstruction by low rank and sparse decomposition adaptively. Background subtraction becomes part of the reconstruction. In our method, a background model is used in which the background is learned adaptively as the compressive measurements are processed. The adaptive method has low latency, and is more robust than previous methods. We will present experimental results to demonstrate the advantages of the proposed method. © 2013 IEEE.
Source Title: 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/125023
ISBN: 9781479923410
DOI: 10.1109/ICIP.2013.6738210
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