Online and Batch Supervised Background Estimation Via L1 Regression
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
KAUST, University of Edinburgh, Moscow Institute of Physics and Technology (MIPT), , , Russian Federation
Permanent link to this recordhttp://hdl.handle.net/10754/626534
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AbstractWe 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.
CitationDutta A, Richtarik P (2019) Online and Batch Supervised Background Estimation Via L1 Regression. 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). Available: http://dx.doi.org/10.1109/WACV.2019.00063.
Conference/Event name19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019