TY - GEN
T1 - A Batch-Incremental Video Background Estimation Model using Weighted Low-Rank Approximation of Matrices
AU - Dutta, Aritra
AU - Li, Xin
AU - Richtarik, Peter
N1 - KAUST Repository Item: Exported on 2021-08-20
PY - 2018/1/19
Y1 - 2018/1/19
N2 - Principal component pursuit (PCP) is a state-of-the-art approach to 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 incrementally. We build a batch-incremental background estimation model by using a special weighted low-rank approximation of matrices. Through experiments with real and synthetic video sequences, we demonstrate that our model is superior to the existing state-of-the-art background estimation algorithms such as GRASTA, ReProCS, incPCP, and GFL.
AB - Principal component pursuit (PCP) is a state-of-the-art approach to 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 incrementally. We build a batch-incremental background estimation model by using a special weighted low-rank approximation of matrices. Through experiments with real and synthetic video sequences, we demonstrate that our model is superior to the existing state-of-the-art background estimation algorithms such as GRASTA, ReProCS, incPCP, and GFL.
UR - http://hdl.handle.net/10754/670694
UR - http://ieeexplore.ieee.org/document/8265427/
UR - http://www.scopus.com/inward/record.url?scp=85046282811&partnerID=8YFLogxK
U2 - 10.1109/ICCVW.2017.217
DO - 10.1109/ICCVW.2017.217
M3 - Conference contribution
SN - 9781538610343
T3 - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
SP - 1835
EP - 1843
BT - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Y2 - 22 October 2017 through 29 October 2017
ER -