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.
|Original language||English (US)|
|Title of host publication||2019 IEEE Winter Conference on Applications of Computer Vision (WACV)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||10|
|State||Published - Mar 8 2019|