Lagrange Motion Analysis and View Embeddings for Improved Gait Recognition
Abstract
Gait is considered the walking pattern of human body, which includes both shape and motion cues. However, the main-stream appearance-based methods for gait recognition rely on the shape of silhouette. It is unclear whether motion can be explicitly represented in the gait sequence modeling. In this paper, we analyzed human walking using the Lagrange's equation and come to the conclusion that second-order information in the temporal dimension is necessary for identification. We designed a second-order motion extraction module based on the conclusions drawn. Also, a light weight view-embedding module is designed by analyzing the problem that current methods to cross-view task do not take view itself into consideration explicitly. Experiments on CASIA-B and OU-MVLP datasets show the effectiveness of our method and some visualization for extracted motion are done to show the interpretability of our motion extraction module.
Cite
Text
Chai et al. "Lagrange Motion Analysis and View Embeddings for Improved Gait Recognition." Conference on Computer Vision and Pattern Recognition, 2022. doi:10.1109/CVPR52688.2022.01961Markdown
[Chai et al. "Lagrange Motion Analysis and View Embeddings for Improved Gait Recognition." Conference on Computer Vision and Pattern Recognition, 2022.](https://mlanthology.org/cvpr/2022/chai2022cvpr-lagrange/) doi:10.1109/CVPR52688.2022.01961BibTeX
@inproceedings{chai2022cvpr-lagrange,
title = {{Lagrange Motion Analysis and View Embeddings for Improved Gait Recognition}},
author = {Chai, Tianrui and Li, Annan and Zhang, Shaoxiong and Li, Zilong and Wang, Yunhong},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2022},
pages = {20249-20258},
doi = {10.1109/CVPR52688.2022.01961},
url = {https://mlanthology.org/cvpr/2022/chai2022cvpr-lagrange/}
}