DocumentCode :
3273510
Title :
Adaptive low rank and sparse decomposition of video using compressive sensing
Author :
Fei Yang ; Hong Jiang ; Zuowei Shen ; Wei Deng ; Metaxas, Dimitris
fYear :
2013
fDate :
15-18 Sept. 2013
Firstpage :
1016
Lastpage :
1020
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.
Keywords :
compressed sensing; data compression; video coding; video surveillance; adaptive low rank; background model; background subtraction; compressive measurements; compressive sensing; sparse decomposition; surveillance videos; video reconstruction; Cameras; Compressed sensing; Computational modeling; Image reconstruction; Matrix decomposition; Optimization; Surveillance; Compressive sensing; background subtraction; low rank and sparse decomposition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2013 20th IEEE International Conference on
Conference_Location :
Melbourne, VIC
Type :
conf
DOI :
10.1109/ICIP.2013.6738210
Filename :
6738210
Link To Document :
بازگشت