Title :
Low rank and sparse matrix reconstruction with partial support knowledge for surveillance video processing
Author :
Zonoobi, Dornoosh ; Kassim, Ashraf A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore
Abstract :
It has been shown recently that incorporating priori knowledge into the basic compressive sensing results in significant improvement of its performance. This has motivated us to extend the incorporation of partial known support into the problem of Robust Principal Component Analysis (RPCA) from compressive measurements. Our proposed algorithm utilizes the known part of the support to recover a matrix as the sum of a low-rank matrix and a sparse component and is tested on the problem of surveillance video reconstruction from compressive measurements. Our experimental results show that the incorporation of partial known support, can significantly improve the reconstruction performance of video sequences.
Keywords :
compressed sensing; image reconstruction; image sequences; matrix algebra; principal component analysis; video surveillance; RPCA; compressive measurements; compressive sensing; low rank reconstruction; low-rank matrix; partial support knowledge; performance improvement; robust principal component analysis; sparse component; sparse matrix reconstruction; surveillance video processing; surveillance video reconstruction; video sequences; Algorithm design and analysis; Image reconstruction; PSNR; Principal component analysis; Robustness; Sparse matrices; Surveillance; Compressive sampling; Robust Principal Component Analysis; Video Compression; partially known support;
Conference_Titel :
Image Processing (ICIP), 2013 20th IEEE International Conference on
Conference_Location :
Melbourne, VIC
DOI :
10.1109/ICIP.2013.6738069