Person Re-Identification in Movies/Dramas

Dong Hyuck Im; Yong Seok Seo; Hyeonwoo Kim; Eenjun Hwang; Jihyun Park

doi:10.1109/ICTC49870.2020.9289375

Person Re-Identification in Movies/Dramas

Dong Hyuck Im, Yong Seok Seo, Hyeonwoo Kim, Eenjun Hwang, Jihyun Park

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

1 Citation (Scopus)

Abstract

In this paper, we present a system for person re-identification in movies and dramas. Our person re-identification method simultaneously learns differences and commonalities between two pairs of images. Features are extracted using three images, and the relationship between the extracted features is constructed into two pairs of feature maps to learn. Our method significantly outperforms on a large data set (CUHK03), and our own re-identification dataset generated from video contents such as movies and dramas. We also show that the proposed model learned on our own data set can be applied to actor re-identification in video content.

Original language	English
Title of host publication	ICTC 2020 - 11th International Conference on ICT Convergence
Subtitle of host publication	Data, Network, and AI in the Age of Untact
Publisher	IEEE Computer Society
Pages	1596-1598
Number of pages	3
ISBN (Electronic)	9781728167589
DOIs	https://doi.org/10.1109/ICTC49870.2020.9289375
Publication status	Published - 2020 Oct 21
Event	11th International Conference on Information and Communication Technology Convergence, ICTC 2020 - Jeju Island, Korea, Republic of Duration: 2020 Oct 21 → 2020 Oct 23

Publication series

Name	International Conference on ICT Convergence
Volume	2020-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	11th International Conference on Information and Communication Technology Convergence, ICTC 2020
Country/Territory	Korea, Republic of
City	Jeju Island
Period	20/10/21 → 20/10/23

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This research was supported by Ministry of Culture, Sports and Tourism (MCST) and Korea Copyright Commission(KCC) in the Copyright Technology Research & Development Program 2020.

Publisher Copyright:
© 2020 IEEE.

Keywords

actor re-identification
deep learning
person re-identification

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications

Access to Document

10.1109/ICTC49870.2020.9289375

Cite this

Im, D. H., Seo, Y. S., Kim, H., Hwang, E., & Park, J. (2020). Person Re-Identification in Movies/Dramas. In ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact (pp. 1596-1598). Article 9289375 (International Conference on ICT Convergence; Vol. 2020-October). IEEE Computer Society. https://doi.org/10.1109/ICTC49870.2020.9289375

Person Re-Identification in Movies/Dramas. / Im, Dong Hyuck; Seo, Yong Seok; Kim, Hyeonwoo et al.
ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact. IEEE Computer Society, 2020. p. 1596-1598 9289375 (International Conference on ICT Convergence; Vol. 2020-October).

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

Im, DH, Seo, YS, Kim, H, Hwang, E & Park, J 2020, Person Re-Identification in Movies/Dramas. in ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact., 9289375, International Conference on ICT Convergence, vol. 2020-October, IEEE Computer Society, pp. 1596-1598, 11th International Conference on Information and Communication Technology Convergence, ICTC 2020, Jeju Island, Korea, Republic of, 20/10/21. https://doi.org/10.1109/ICTC49870.2020.9289375

@inproceedings{a9ff0a27468b4ec98704907576e79abf,

title = "Person Re-Identification in Movies/Dramas",

abstract = "In this paper, we present a system for person re-identification in movies and dramas. Our person re-identification method simultaneously learns differences and commonalities between two pairs of images. Features are extracted using three images, and the relationship between the extracted features is constructed into two pairs of feature maps to learn. Our method significantly outperforms on a large data set (CUHK03), and our own re-identification dataset generated from video contents such as movies and dramas. We also show that the proposed model learned on our own data set can be applied to actor re-identification in video content.",

keywords = "actor re-identification, deep learning, person re-identification",

author = "Im, {Dong Hyuck} and Seo, {Yong Seok} and Hyeonwoo Kim and Eenjun Hwang and Jihyun Park",

note = "Funding Information: ACKNOWLEDGMENT This research was supported by Ministry of Culture, Sports and Tourism (MCST) and Korea Copyright Commission(KCC) in the Copyright Technology Research & Development Program 2020. Publisher Copyright: {\textcopyright} 2020 IEEE.; 11th International Conference on Information and Communication Technology Convergence, ICTC 2020 ; Conference date: 21-10-2020 Through 23-10-2020",

year = "2020",

month = oct,

day = "21",

doi = "10.1109/ICTC49870.2020.9289375",

language = "English",

series = "International Conference on ICT Convergence",

publisher = "IEEE Computer Society",

pages = "1596--1598",

booktitle = "ICTC 2020 - 11th International Conference on ICT Convergence",

}

TY - GEN

T1 - Person Re-Identification in Movies/Dramas

AU - Im, Dong Hyuck

AU - Seo, Yong Seok

AU - Kim, Hyeonwoo

AU - Hwang, Eenjun

AU - Park, Jihyun

N1 - Funding Information: ACKNOWLEDGMENT This research was supported by Ministry of Culture, Sports and Tourism (MCST) and Korea Copyright Commission(KCC) in the Copyright Technology Research & Development Program 2020. Publisher Copyright: © 2020 IEEE.

PY - 2020/10/21

Y1 - 2020/10/21

N2 - In this paper, we present a system for person re-identification in movies and dramas. Our person re-identification method simultaneously learns differences and commonalities between two pairs of images. Features are extracted using three images, and the relationship between the extracted features is constructed into two pairs of feature maps to learn. Our method significantly outperforms on a large data set (CUHK03), and our own re-identification dataset generated from video contents such as movies and dramas. We also show that the proposed model learned on our own data set can be applied to actor re-identification in video content.

AB - In this paper, we present a system for person re-identification in movies and dramas. Our person re-identification method simultaneously learns differences and commonalities between two pairs of images. Features are extracted using three images, and the relationship between the extracted features is constructed into two pairs of feature maps to learn. Our method significantly outperforms on a large data set (CUHK03), and our own re-identification dataset generated from video contents such as movies and dramas. We also show that the proposed model learned on our own data set can be applied to actor re-identification in video content.

KW - actor re-identification

KW - deep learning

KW - person re-identification

UR - http://www.scopus.com/inward/record.url?scp=85098945497&partnerID=8YFLogxK

U2 - 10.1109/ICTC49870.2020.9289375

DO - 10.1109/ICTC49870.2020.9289375

M3 - Conference contribution

AN - SCOPUS:85098945497

T3 - International Conference on ICT Convergence

SP - 1596

EP - 1598

BT - ICTC 2020 - 11th International Conference on ICT Convergence

PB - IEEE Computer Society

T2 - 11th International Conference on Information and Communication Technology Convergence, ICTC 2020

Y2 - 21 October 2020 through 23 October 2020

ER -

Person Re-Identification in Movies/Dramas

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this