Online Adaptive Gaussian Mixture Learning for Video Applications
Abstract
This paper presents an online EM learning algorithm for training adaptive Gaussian mixtures for non-stationary video data. Existing solutions are either slow in learning or computationally and storage inefficient. Our solution is derived based on sufficient statistics of the short-term distribution. To avoid unnecessary computation or storage, we show that the equivalent estimates can be accomplished by a set of recursive parameter update equations with one additional variable. The solution is evaluated against several existing algorithms on both synthetic data and surveillance videos. The results showed remarkable learning efficiency and robustness over current solutions.
Cite
Text
Lee. "Online Adaptive Gaussian Mixture Learning for Video Applications." European Conference on Computer Vision, 2004. doi:10.1007/978-3-540-30212-4_10Markdown
[Lee. "Online Adaptive Gaussian Mixture Learning for Video Applications." European Conference on Computer Vision, 2004.](https://mlanthology.org/eccv/2004/lee2004eccv-online/) doi:10.1007/978-3-540-30212-4_10BibTeX
@inproceedings{lee2004eccv-online,
title = {{Online Adaptive Gaussian Mixture Learning for Video Applications}},
author = {Lee, Dar-Shyang},
booktitle = {European Conference on Computer Vision},
year = {2004},
pages = {105-116},
doi = {10.1007/978-3-540-30212-4_10},
url = {https://mlanthology.org/eccv/2004/lee2004eccv-online/}
}