Person Re-Identification by Symmetry-Driven Accumulation of Local Features
Abstract
In this paper, we present an appearance-based method for person re-identification. It consists in the extraction of features that model three complementary aspects of the human appearance: the overall chromatic content, the spatial arrangement of colors into stable regions, and the presence of recurrent local motifs with high entropy. All this information is derived from different body parts, and weighted opportunely by exploiting symmetry and asymmetry perceptual principles. In this way, robustness against very low resolution, occlusions and pose, viewpoint and illumination changes is achieved. The approach applies to situations where the number of candidates varies continuously, considering single images or bunch of frames for each individual. It has been tested on several public benchmark datasets (ViPER, iLIDS, ETHZ), gaining new state-of-the-art performances.
Cite
Text
Farenzena et al. "Person Re-Identification by Symmetry-Driven Accumulation of Local Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010. doi:10.1109/CVPR.2010.5539926Markdown
[Farenzena et al. "Person Re-Identification by Symmetry-Driven Accumulation of Local Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010.](https://mlanthology.org/cvpr/2010/farenzena2010cvpr-person/) doi:10.1109/CVPR.2010.5539926BibTeX
@inproceedings{farenzena2010cvpr-person,
title = {{Person Re-Identification by Symmetry-Driven Accumulation of Local Features}},
author = {Farenzena, Michela and Bazzani, Loris and Perina, Alessandro and Murino, Vittorio and Cristani, Marco},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2010},
pages = {2360-2367},
doi = {10.1109/CVPR.2010.5539926},
url = {https://mlanthology.org/cvpr/2010/farenzena2010cvpr-person/}
}