Change Detection with Weightless Neural Networks
Abstract
In this paper a pixel -- based Weightless Neural Network (WNN) method to face the problem of change detection in the field of view of a camera is proposed. The main features of the proposed method are 1) the dynamic adaptability to background change due to the WNN model adopted and 2) the introduction of pixel color histories to improve system behavior in videos characterized by (des)appearing of objects in video scene and/or sudden changes in lightning and background brightness and shape. The WNN approach is very simple and straightforward, and it gives high rank results in competition with other approaches applied to the ChangeDetection.net 2014 benchmark dataset.
Cite
Text
De Gregorio and Giordano. "Change Detection with Weightless Neural Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2014. doi:10.1109/CVPRW.2014.66Markdown
[De Gregorio and Giordano. "Change Detection with Weightless Neural Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2014.](https://mlanthology.org/cvprw/2014/gregorio2014cvprw-change/) doi:10.1109/CVPRW.2014.66BibTeX
@inproceedings{gregorio2014cvprw-change,
title = {{Change Detection with Weightless Neural Networks}},
author = {De Gregorio, Massimo and Giordano, Maurizio},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2014},
pages = {409-413},
doi = {10.1109/CVPRW.2014.66},
url = {https://mlanthology.org/cvprw/2014/gregorio2014cvprw-change/}
}