TY - GEN
T1 - Online and Batch Supervised Background Estimation Via L1 Regression
AU - Dutta, Aritra
AU - Richtarik, Peter
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2019/3/8
Y1 - 2019/3/8
N2 - We propose a surprisingly simple model to estimate supervised video backgrounds. Our model is based on L1 regression. As existing methods for L1 regression do not scale to high-resolution videos, we propose several simple, fast, and scalable methods including iteratively reweighted least squares, a homotopy method, and stochastic gradient descent to solve the problem. Our extensive implementations of the model and methods show that they match or outperform other state-of-the-art online and batch methods that are both supervised and unsupervised in virtually all quantitative and qualitative measures and in fractions of their execution time.
AB - We propose a surprisingly simple model to estimate supervised video backgrounds. Our model is based on L1 regression. As existing methods for L1 regression do not scale to high-resolution videos, we propose several simple, fast, and scalable methods including iteratively reweighted least squares, a homotopy method, and stochastic gradient descent to solve the problem. Our extensive implementations of the model and methods show that they match or outperform other state-of-the-art online and batch methods that are both supervised and unsupervised in virtually all quantitative and qualitative measures and in fractions of their execution time.
UR - http://hdl.handle.net/10754/626534
UR - https://ieeexplore.ieee.org/document/8659272
UR - http://www.scopus.com/inward/record.url?scp=85063565649&partnerID=8YFLogxK
U2 - 10.1109/WACV.2019.00063
DO - 10.1109/WACV.2019.00063
M3 - Conference contribution
SN - 9781728119755
SP - 541
EP - 550
BT - 2019 IEEE Winter Conference on Applications of Computer Vision (WACV)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -