Camera-Aware Image-to-Image Translation Using Similarity Preserving StarGAN for Person Re-Identification
Abstract
Person re-identification is a crucial task in intelligent video surveillance systems. It can be defined as recognizing the same person from images of a person taken from different cameras at different times. In this paper, we present a camera-aware image-to-image translation using similarity preserving StarGAN (SP-StarGAN) as the data augmentation for person re-identification. We propose the addition of an identity mapping term and a multi-scale structural similarity term as additional losses for the generator. SP-StarGAN can learn the relationship among the multiple cameras with a single model and generate the camera-aware extra training samples for person re-identification. We evaluate our proposed method on public datasets (Market-1501 and DukeMTMC-reID) and demonstrate the efficacy of our method. We also report competitive performance with the state-of-the-art methods.
Cite
Text
Chung and Delp. "Camera-Aware Image-to-Image Translation Using Similarity Preserving StarGAN for Person Re-Identification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019. doi:10.1109/CVPRW.2019.00193Markdown
[Chung and Delp. "Camera-Aware Image-to-Image Translation Using Similarity Preserving StarGAN for Person Re-Identification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/chung2019cvprw-cameraaware/) doi:10.1109/CVPRW.2019.00193BibTeX
@inproceedings{chung2019cvprw-cameraaware,
title = {{Camera-Aware Image-to-Image Translation Using Similarity Preserving StarGAN for Person Re-Identification}},
author = {Chung, Dahjung and Delp, Edward J.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2019},
pages = {1517-1525},
doi = {10.1109/CVPRW.2019.00193},
url = {https://mlanthology.org/cvprw/2019/chung2019cvprw-cameraaware/}
}