Deep Feature Fusion with Multiple Granularity for Vehicle Re-Identification
Abstract
Vehicle re-identification (Re-Id) plays a significant role in modern life. We found that Vehicle Re-Id and Person Re-Id are two very similar tasks in the field of Re-Id. To some extent, the Person Re-Id Networks can be transplanted to the Vehicle Re-Id tasks. In this paper, a Deep Feature Fusion with Multiple Granularity (DFFMG) method for Vehicle Re-Id is proposed for integrating discriminative information with various granularity. DFFMG is based on the Multiple Granularity Network (MGN), the state-of-the-art method from Person Re-Id. We pondered on the discrimination between Vehicle Re-Id and Person Re-Id. And we carefully designed DFFMG: a multi-branch deep network architecture which consists of one branch for global feature representations, two for vertical local feature representations and other two horizontal ones. Besides, several re-ranking methods were tested in our experiments and achieved higher scores. This network is adopted to train and test on the 2019 NVIDIA AI City Dataset [16]
Cite
Text
Huang et al. "Deep Feature Fusion with Multiple Granularity for Vehicle Re-Identification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.Markdown
[Huang et al. "Deep Feature Fusion with Multiple Granularity for Vehicle Re-Identification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/huang2019cvprw-deep-a/)BibTeX
@inproceedings{huang2019cvprw-deep-a,
title = {{Deep Feature Fusion with Multiple Granularity for Vehicle Re-Identification}},
author = {Huang, Peixiang and Huang, Runhui and Huang, Jianjie and Yangchen, Rushi and He, Zongyao and Li, Xiying and Chen, Junzhou},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2019},
pages = {80-88},
url = {https://mlanthology.org/cvprw/2019/huang2019cvprw-deep-a/}
}