Principal component pursuit (PCP) is a state-of-the-art approach for background estimation problems. Due to their higher computational cost, PCP algorithms, such as robust principal component analysis (RPCA) and its variants, are not feasible in processing high definition videos. To avoid the curse of dimensionality in those algorithms, several methods have been proposed to solve the background estimation problem in an incremental manner. We propose a batch-incremental background estimation model using a special weighted low-rank approximation of matrices. Through experiments with real and synthetic video sequences, we demonstrate that our method is superior to the state-of-the-art background estimation algorithms such as GRASTA, ReProCS, incPCP, and GFL.
|Title of host publication||IEEE International Conference on Computer Vision (ICCV) Workshops, 2017|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||10|
|Publication status||E-pub ahead of print - 22 Oct 2017|