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Analysis of Recent Re-Identification Architectures for Tracking-by-Detection Paradigm in Multi-Object Tracking

Topics: Categorization and Scene Understanding; Deep Learning for Tracking; Deep Learning for Visual Understanding ; Event and Human Activity Recognition; Object Detection and Localization; Video Surveillance and Event Detection

Authors: Haruya Ishikawa 1 ; Masaki Hayashi 1 ; Trong Huy Phan 2 ; Kazuma Yamamoto 2 ; Makoto Masuda 2 and Yoshimitsu Aoki 1

Affiliations: 1 Department of Electrical Engineering, Keio University, Yokohama, Japan ; 2 OKI Electric Industry Co., Ltd., Saitama, Japan

Keyword(s): Multi-Object Tracking, Person Re-Identification, Video Re-Identification, Metric Learning.

Abstract: Person re-identification is a vital module of the tracking-by-detection framework for online multi-object tracking. Despite recent advances in multi-object tracking and person re-identification, inadequate attention was given to integrating these technologies to provide a robust multi-object tracker. In this work, we combine modern state-of-the-art re-identification models and modeling techniques on the basic tracking-by-detection framework and benchmark them on heavily occluded scenes to understand their effect. We hypothesize that temporal modeling for re-identification is crucial for training robust re-identification models for they are conditioned on sequences containing occlusions. Along with traditional image-based re-identification methods, we analyze temporal modeling methods used in video-based re-identification tasks. We also train re-identification models with different embedding methods, including triplet loss, and analyze their effect. We benchmark the re-identification models on the challenging MOT20 dataset containing crowded scenes with various occlusions. We provide a thorough assessment and investigation of the usage of modern re-identification modeling methods and prove that these methods are, in fact, effective for multi-object tracking. Compared to baseline methods, results show that these models can provide robust re-identification proved by improvements in the number of identity switching, MOTA, IDF1, and other metrics. (More)

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Paper citation in several formats:
Ishikawa, H.; Hayashi, M.; Phan, T.; Yamamoto, K.; Masuda, M. and Aoki, Y. (2021). Analysis of Recent Re-Identification Architectures for Tracking-by-Detection Paradigm in Multi-Object Tracking. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 234-244. DOI: 10.5220/0010341502340244

@conference{visapp21,
author={Haruya Ishikawa. and Masaki Hayashi. and Trong Huy Phan. and Kazuma Yamamoto. and Makoto Masuda. and Yoshimitsu Aoki.},
title={Analysis of Recent Re-Identification Architectures for Tracking-by-Detection Paradigm in Multi-Object Tracking},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={234-244},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010341502340244},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - Analysis of Recent Re-Identification Architectures for Tracking-by-Detection Paradigm in Multi-Object Tracking
SN - 978-989-758-488-6
IS - 2184-4321
AU - Ishikawa, H.
AU - Hayashi, M.
AU - Phan, T.
AU - Yamamoto, K.
AU - Masuda, M.
AU - Aoki, Y.
PY - 2021
SP - 234
EP - 244
DO - 10.5220/0010341502340244
PB - SciTePress