Re-Identification for Multi-Object Tracking Using Triplet Loss

Koung Suk Ko; Woo Jin Ahn; Geon Hee Kim; Myo Taeg Lim; Tae Koo Kang; Dong Sung Pae

doi:10.1109/ICOIN50884.2021.9334017

Re-Identification for Multi-Object Tracking Using Triplet Loss

Koung Suk Ko, Woo Jin Ahn, Geon Hee Kim, Myo Taeg Lim, Tae Koo Kang, Dong Sung Pae

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Assigning a consistent identification(ID) number is a chronic problem in the tracking model. However, recent tracking models lose the ID because it focuses only on the previous frame. This paper constructed a tracking deep learning model using triplet loss to give consistent ID to objects detected while tracking. We also show the best way for pre-processing the input for the triplet-tracking model, which inputs various image sizes. The experimental result of 97.76% accuracy on KITTI shows the effectiveness of our result.

Original language	English
Title of host publication	35th International Conference on Information Networking, ICOIN 2021
Publisher	IEEE Computer Society
Pages	525-527
Number of pages	3
ISBN (Electronic)	9781728191003
DOIs	https://doi.org/10.1109/ICOIN50884.2021.9334017
Publication status	Published - 2021 Jan 13
Event	35th International Conference on Information Networking, ICOIN 2021 - Jeju Island, Korea, Republic of Duration: 2021 Jan 13 → 2021 Jan 16

Publication series

Name	International Conference on Information Networking
Volume	2021-January
ISSN (Print)	1976-7684

Conference

Conference	35th International Conference on Information Networking, ICOIN 2021
Country/Territory	Korea, Republic of
City	Jeju Island
Period	21/1/13 → 21/1/16

Keywords

Metric Learning
Multi-Object Tracking
Re-Identification
Triplet Loss

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems

Access to Document

10.1109/ICOIN50884.2021.9334017

Cite this

Ko, K. S., Ahn, W. J., Kim, G. H., Lim, M. T., Kang, T. K., & Pae, D. S. (2021). Re-Identification for Multi-Object Tracking Using Triplet Loss. In 35th International Conference on Information Networking, ICOIN 2021 (pp. 525-527). Article 9334017 (International Conference on Information Networking; Vol. 2021-January). IEEE Computer Society. https://doi.org/10.1109/ICOIN50884.2021.9334017

Re-Identification for Multi-Object Tracking Using Triplet Loss. / Ko, Koung Suk; Ahn, Woo Jin; Kim, Geon Hee et al.
35th International Conference on Information Networking, ICOIN 2021. IEEE Computer Society, 2021. p. 525-527 9334017 (International Conference on Information Networking; Vol. 2021-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ko, KS, Ahn, WJ, Kim, GH, Lim, MT, Kang, TK & Pae, DS 2021, Re-Identification for Multi-Object Tracking Using Triplet Loss. in 35th International Conference on Information Networking, ICOIN 2021., 9334017, International Conference on Information Networking, vol. 2021-January, IEEE Computer Society, pp. 525-527, 35th International Conference on Information Networking, ICOIN 2021, Jeju Island, Korea, Republic of, 21/1/13. https://doi.org/10.1109/ICOIN50884.2021.9334017

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abstract = "Assigning a consistent identification(ID) number is a chronic problem in the tracking model. However, recent tracking models lose the ID because it focuses only on the previous frame. This paper constructed a tracking deep learning model using triplet loss to give consistent ID to objects detected while tracking. We also show the best way for pre-processing the input for the triplet-tracking model, which inputs various image sizes. The experimental result of 97.76% accuracy on KITTI shows the effectiveness of our result.",

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AB - Assigning a consistent identification(ID) number is a chronic problem in the tracking model. However, recent tracking models lose the ID because it focuses only on the previous frame. This paper constructed a tracking deep learning model using triplet loss to give consistent ID to objects detected while tracking. We also show the best way for pre-processing the input for the triplet-tracking model, which inputs various image sizes. The experimental result of 97.76% accuracy on KITTI shows the effectiveness of our result.

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Re-Identification for Multi-Object Tracking Using Triplet Loss

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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