A Batch-Incremental Video Background Estimation Model using Weighted Low-Rank Approximation of Matrices
This work addresses computational efficiency for video processing applications, but it is incremental as it builds on existing incremental methods.
The paper tackled the problem of high computational cost in video background estimation by proposing a batch-incremental model using weighted low-rank approximation, demonstrating superiority over state-of-the-art methods like GRASTA, ReProCS, incPCP, and GFL in experiments with real and synthetic videos.
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.