Static and Moving Object Detection Using Flux Tensor with Split Gaussian Models
Abstract
In this paper, we present a moving object detection system named Flux Tensor with Split Gaussian models (FTSG) that exploits the benefits of fusing a motion computation method based on spatio-temporal tensor formulation, a novel foreground and background modeling scheme, and a multi-cue appearance comparison. This hybrid system can handle challenges such as shadows, illumination changes, dynamic background, stopped and removed objects. Extensive testing performed on the CVPR 2014 Change Detection benchmark dataset shows that FTSG outperforms state-of-the-art methods.
Cite
Text
Wang et al. "Static and Moving Object Detection Using Flux Tensor with Split Gaussian Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2014. doi:10.1109/CVPRW.2014.68Markdown
[Wang et al. "Static and Moving Object Detection Using Flux Tensor with Split Gaussian Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2014.](https://mlanthology.org/cvprw/2014/wang2014cvprw-static/) doi:10.1109/CVPRW.2014.68BibTeX
@inproceedings{wang2014cvprw-static,
title = {{Static and Moving Object Detection Using Flux Tensor with Split Gaussian Models}},
author = {Wang, Rui and Bunyak, Filiz and Seetharaman, Guna and Palaniappan, Kannappan},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2014},
pages = {420-424},
doi = {10.1109/CVPRW.2014.68},
url = {https://mlanthology.org/cvprw/2014/wang2014cvprw-static/}
}