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CAIP
2015
Springer

Incremental Fixed-Rank Robust PCA for Video Background Recovery

7 years 10 months ago
Incremental Fixed-Rank Robust PCA for Video Background Recovery
Abstract. Video background recovery is a very important task in computer vision applications. Recent research offers robust principal component analysis (RPCA) as a promising approach for solving video background recovery. RPCA works by decomposing a data matrix into a low-rank matrix and a sparse matrix. Our previous work shows that when the desired rank of the low-rank matrix is known, fixing the rank in the algorithm called FrALM (fixed-rank ALM) yields more robust and accurate results than existing RPCA algorithms. However, application of RPCA to video background recovery requires that each frame in the video is encoded as a column in the data matrix. This is impractical in real applications because the videos can be easily larger than the amount of memory in a computer. This paper presents an algorithm called iFrALM (incremental fixed-rank ALM) that computes fixed-rank RPCA incrementally by splitting the video frames into an initial batch and an incremental batch. Comprehensi...
Jian Lai, Wee Kheng Leow, Terence Sim
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where CAIP
Authors Jian Lai, Wee Kheng Leow, Terence Sim
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