Evaluation Report of Integrated Background Modeling Based on Spatio-Temporal Features
Abstract
We report evaluation results of an integrated background modeling based on spatio-temporal features. The background modeling method consists of three complementary approaches: pixel-level background modeling, region-level one and frame-level one. The pixel-level background model uses the probability density function to approximate background model. The PDF is estimated non-parametrically by using Parzen density estimation. The region-level model is based on the evaluation of the local texture around each pixel while reducing the effects of variations in lighting. The frame-level model detects sudden, global changes of the the image brightness and estimates a present background image from input image referring to a background model image. Then, objects are extracted by background subtraction. Fusing these approaches realizes robust object detection under varying illumination.
Cite
Text
Nonaka et al. "Evaluation Report of Integrated Background Modeling Based on Spatio-Temporal Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2012. doi:10.1109/CVPRW.2012.6238920Markdown
[Nonaka et al. "Evaluation Report of Integrated Background Modeling Based on Spatio-Temporal Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2012.](https://mlanthology.org/cvprw/2012/nonaka2012cvprw-evaluation/) doi:10.1109/CVPRW.2012.6238920BibTeX
@inproceedings{nonaka2012cvprw-evaluation,
title = {{Evaluation Report of Integrated Background Modeling Based on Spatio-Temporal Features}},
author = {Nonaka, Yosuke and Shimada, Atsushi and Nagahara, Hajime and Taniguchi, Rin-Ichiro},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2012},
pages = {9-14},
doi = {10.1109/CVPRW.2012.6238920},
url = {https://mlanthology.org/cvprw/2012/nonaka2012cvprw-evaluation/}
}