2017/07/02 by Aritra Dutta, Xin Li, Dutta, Aritra +3
Computer Science · Engineering · #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Mathematics #Image Enhancement Techniques #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Video Analysis and Summarization #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.1707.00281
openalex publication_date 2017/07/02 · openalex created_date 2022/11/14 · openalex updated_date 2026/07/28
Principal component pursuit (PCP) is a state-of-the-art approach for\nbackground estimation problems. Due to their higher computational cost, PCP\nalgorithms, such as robust principal component analysis (RPCA) and its\nvariants, are not feasible in processing high definition videos. To avoid the\ncurse of dimensionality in those algorithms, several methods have been proposed\nto solve the background estimation problem in an incremental manner. We propose\na batch-incremental background estimation model using a special weighted\nlow-rank approximation of matrices. Through experiments with real and synthetic\nvideo sequences, we demonstrate that our method is superior to the\nstate-of-the-art background estimation algorithms such as GRASTA, ReProCS,\nincPCP, and GFL.\n