Similarity Learning for Person Re-Identification Using Deep Auto-Encoder

Sevdenur Kutuk, Rayan Abri, Sara Abri, Salih Cetin

2023

Abstract

Person re-identification (ReID) has been one of the most crucial issues in computer vision, particularly for reasons of security and privacy. Person re-identification generally aims to create a unique identity for a person seen in the field of view of a camera and to identify the same person in different frames of the same camera or within the relevant frames of multiple cameras. Due to low resolution and noisy frames, crowded scenes, scenes with occlusion, weather, and light changes, and data sets with insufficient numbers of samples containing different states of the same person for training supervised models, person re-identification remains a challenging and studied problem. In this paper, we propose a hybrid person re-identification model that uses Normalized Cross-Correlation (NCC) and cosine similarity to determine whether extracted features belong to the same person, which we call DAE-ID (Deep Auto-Encoder Identification). The model is built using a pre-trained You Only Look Once Version 4 (YOLOV4) algorithm to detect objects and a convolutional auto-encoder trained on the Motion Analysis and Re-identification Set (Mars) data set for feature extraction. Our method outperforms state-of-the-art methods while outperforming them on the Chinese University of Hong Kong (CUHK03) with 0.966 rank-1 and 0.857 mAP and Duke Multi-Tracking Multi-Camera Re-Identification (DukeMTMC-reID) with 0.956 rank-1 and 0.841 mAP for single-person re-identification.

Paper Citation

in Harvard Style

Kutuk S., Abri R., Abri S. and Cetin S. (2023). Similarity Learning for Person Re-Identification Using Deep Auto-Encoder. In Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-672-9, SciTePress, pages 520-527. DOI: 10.5220/0012253900003584

in Bibtex Style

@conference{webist23,
author={Sevdenur Kutuk and Rayan Abri and Sara Abri and Salih Cetin},
title={Similarity Learning for Person Re-Identification Using Deep Auto-Encoder},
booktitle={Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2023},
pages={520-527},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012253900003584},
isbn={978-989-758-672-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - Similarity Learning for Person Re-Identification Using Deep Auto-Encoder
SN - 978-989-758-672-9
AU - Kutuk S.
AU - Abri R.
AU - Abri S.
AU - Cetin S.
PY - 2023
SP - 520
EP - 527
DO - 10.5220/0012253900003584
PB - SciTePress