Background Modeling and Subtraction of Dynamic Scenes
Abstract
Background modeling and subtraction is a core component in motion analysis. The central idea behind such module is to create a probabilistic representation of the static scene that is compared with the current input to perform subtraction. Such approach is efficient when the scene to be modeled refers to a static structure with limited perturbation. In this paper, we address the problem of modeling dynamic scenes where the assumption of a static background is not valid. Waving trees, beaches, escalators, natural scenes with rain or snow are examples. Inspired by the work proposed in [4], we propose an on-line auto-regressive model to capture and predict the behavior of such scenes. Towards detection of events we introduce a new metric that is based on a state-driven comparison between the prediction and the actual frame. Promising results demonstrate the potentials of the proposed framework. 1
Cite
Text
Monnet et al. "Background Modeling and Subtraction of Dynamic Scenes." IEEE/CVF International Conference on Computer Vision, 2003. doi:10.1109/ICCV.2003.1238641Markdown
[Monnet et al. "Background Modeling and Subtraction of Dynamic Scenes." IEEE/CVF International Conference on Computer Vision, 2003.](https://mlanthology.org/iccv/2003/monnet2003iccv-background/) doi:10.1109/ICCV.2003.1238641BibTeX
@inproceedings{monnet2003iccv-background,
title = {{Background Modeling and Subtraction of Dynamic Scenes}},
author = {Monnet, Antoine and Mittal, Anurag and Paragios, Nikos and Ramesh, Visvanathan},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2003},
pages = {1305-1312},
doi = {10.1109/ICCV.2003.1238641},
url = {https://mlanthology.org/iccv/2003/monnet2003iccv-background/}
}