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

Haruya Ishikawa, Masaki Hayashi, Trong Phan, Kazuma Yamamoto, Makoto Masuda, Yoshimitsu Aoki

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.

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Paper Citation


in Harvard Style

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 - Volume 4: VISAPP, ISBN 978-989-758-488-6, pages 234-244. DOI: 10.5220/0010341502340244


in Bibtex Style

@conference{visapp21,
author={Haruya Ishikawa and Masaki Hayashi and Trong 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 - Volume 4: VISAPP,},
year={2021},
pages={234-244},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010341502340244},
isbn={978-989-758-488-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Analysis of Recent Re-Identification Architectures for Tracking-by-Detection Paradigm in Multi-Object Tracking
SN - 978-989-758-488-6
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