Higher Order Statistical Learning for Vehicle Detection in Images
Abstract
The paper describes a scheme for detecting vehicles in images. The proposed method approximately models the unknown distribution of the images of vehicles by learning higher order statistics (HOS) information of the `vehicle class' from sample images. Given a test image, statistical information about the background is learnt `on the y'. An HOS-based decision measure then classi es test patterns as vehicles or otherwise. When tested onreal images of aerial views of vehicular activity, the method gives good results even on complicated scenes. It does not require any a priori information about the site. However, it is amenable to augmentation with contextual information. The method can serve as an important step towards building an automated roadway monitoring system.
Cite
Text
Rajagopalan et al. "Higher Order Statistical Learning for Vehicle Detection in Images." IEEE/CVF International Conference on Computer Vision, 1999. doi:10.1109/ICCV.1999.790417Markdown
[Rajagopalan et al. "Higher Order Statistical Learning for Vehicle Detection in Images." IEEE/CVF International Conference on Computer Vision, 1999.](https://mlanthology.org/iccv/1999/rajagopalan1999iccv-higher/) doi:10.1109/ICCV.1999.790417BibTeX
@inproceedings{rajagopalan1999iccv-higher,
title = {{Higher Order Statistical Learning for Vehicle Detection in Images}},
author = {Rajagopalan, A. N. and Burlina, Philippe and Chellappa, Rama},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {1999},
pages = {1204-1209},
doi = {10.1109/ICCV.1999.790417},
url = {https://mlanthology.org/iccv/1999/rajagopalan1999iccv-higher/}
}