Benchmarking Person Re-Identification Datasets and Approaches for Practical Real-World Implementations

Jose Huaman, Felix Sumari H., Luigy Machaca, Esteban Clua, Joris Guérin

2023

Abstract

Person Re-Identification (Re-ID) is receiving a lot of attention. Large datasets containing labeled images of various individuals have been released, and successful approaches were developed. However, when Re-ID models are deployed in new cities or environments, they face an important domain shift (ethnicity, clothing, weather, architecture, etc.), resulting in decreased performance. In addition, the whole frames of the video streams must be converted into cropped images of people using pedestrian detection models, which behave differently from the human annotators who built the training dataset. To better understand the extent of this issue, this paper introduces a complete methodology to evaluate Re-ID approaches and training datasets with respect to their suitability for unsupervised deployment for live operations. We benchmark four Re-ID approaches on three datasets, providing insight and guidelines that can help to design better Re-ID pipelines.

Paper Citation

in Harvard Style

Huaman J., Sumari H. F., Machaca L., Clua E. and Guérin J. (2023). Benchmarking Person Re-Identification Datasets and Approaches for Practical Real-World Implementations. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 495-502. DOI: 10.5220/0011633800003417

in Bibtex Style

@conference{visapp23,
author={Jose Huaman and Felix Sumari H. and Luigy Machaca and Esteban Clua and Joris Guérin},
title={Benchmarking Person Re-Identification Datasets and Approaches for Practical Real-World Implementations},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={495-502},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011633800003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - Benchmarking Person Re-Identification Datasets and Approaches for Practical Real-World Implementations
SN - 978-989-758-634-7
AU - Huaman J.
AU - Sumari H. F.
AU - Machaca L.
AU - Clua E.
AU - Guérin J.
PY - 2023
SP - 495
EP - 502
DO - 10.5220/0011633800003417
PB - SciTePress