Late Fusion for Person Detection in Camera Networks
Abstract
In this paper, we present a novel method to detect multiple partially occluded persons in multi-view camera networks. We present a new fusion scheme to integrate the output of part-based object detectors from multiple camera views. This is achieved using subtle and precise modeling of detection and projection uncertainties as well as a fusion method based on probabilistic kernel density estimation. Using a multi-view setup also allows to incorporate additional real-world prior knowledge about person appearances, which not only speeds up processing, but also increases detection rates. Experiments show that this multi-camera approach outperforms methods based on a single perspective, particularly in occlusion-intense scenarios.
Cite
Text
Hofmann et al. "Late Fusion for Person Detection in Camera Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2011. doi:10.1109/CVPRW.2011.5981737Markdown
[Hofmann et al. "Late Fusion for Person Detection in Camera Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2011.](https://mlanthology.org/cvprw/2011/hofmann2011cvprw-late/) doi:10.1109/CVPRW.2011.5981737BibTeX
@inproceedings{hofmann2011cvprw-late,
title = {{Late Fusion for Person Detection in Camera Networks}},
author = {Hofmann, Martin and Kiechle, Martin and Rigoll, Gerhard},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2011},
pages = {41-46},
doi = {10.1109/CVPRW.2011.5981737},
url = {https://mlanthology.org/cvprw/2011/hofmann2011cvprw-late/}
}