Vehicle Re-Identification System for Road Side Unit Application

Xiu Zhi Chen; Chi Peng; Mu Chuan Li; Yen Lin Chen

doi:10.1117/12.2626089

Vehicle Re-Identification System for Road Side Unit Application

Xiu Zhi Chen
, Chi Peng
, Mu Chuan Li
, Yen Lin Chen

Department of Computer Science and Information Engineering

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

Abstract

Vehicle re-identification is one of the complex smart traffic issues, due to the large intra-class variation and high inter-class similarity, it is hard to be solved through traditional hand-crafted features. In this paper, we proposed a vehicle re-identification system based on deep learning techniques, which is able to re-identified the vehicles through deep features under acceptable operation time on Nvidia Jetson TX2. We have collected multiple sequences captured from real-world road side units (RSU) for system evaluation experiments, and the results indicate that it is highly possible to be adopted for real world traffic applications.

Original language	English
Title of host publication	International Workshop on Advanced Imaging Technology, IWAIT 2022
Editors	Masayuki Nakajima, Shogo Muramatsu, Jae-Gon Kim, Jing-Ming Guo, Qian Kemao
Publisher	SPIE
ISBN (Electronic)	9781510653313
DOIs	https://doi.org/10.1117/12.2626089
State	Published - 2022
Event	2022 International Workshop on Advanced Imaging Technology, IWAIT 2022 - Hong Kong, China Duration: 4 Jan 2022 → 6 Jan 2022

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	12177
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	2022 International Workshop on Advanced Imaging Technology, IWAIT 2022
Country/Territory	China
City	Hong Kong
Period	4/01/22 → 6/01/22

Bibliographical note

Publisher Copyright:
© 2022 SPIE.

Keywords

Re-Identification
deep learning
road side unit application

Access to Document

10.1117/12.2626089

Cite this

Chen, X. Z., Peng, C., Li, M. C., & Chen, Y. L. (2022). Vehicle Re-Identification System for Road Side Unit Application. In M. Nakajima, S. Muramatsu, J.-G. Kim, J.-M. Guo, & Q. Kemao (Eds.), International Workshop on Advanced Imaging Technology, IWAIT 2022 Article 121770V (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 12177). SPIE. https://doi.org/10.1117/12.2626089

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title = "Vehicle Re-Identification System for Road Side Unit Application",

abstract = "Vehicle re-identification is one of the complex smart traffic issues, due to the large intra-class variation and high inter-class similarity, it is hard to be solved through traditional hand-crafted features. In this paper, we proposed a vehicle re-identification system based on deep learning techniques, which is able to re-identified the vehicles through deep features under acceptable operation time on Nvidia Jetson TX2. We have collected multiple sequences captured from real-world road side units (RSU) for system evaluation experiments, and the results indicate that it is highly possible to be adopted for real world traffic applications.",

keywords = "Re-Identification, deep learning, road side unit application",

author = "Chen, \{Xiu Zhi\} and Chi Peng and Li, \{Mu Chuan\} and Chen, \{Yen Lin\}",

note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; 2022 International Workshop on Advanced Imaging Technology, IWAIT 2022 ; Conference date: 04-01-2022 Through 06-01-2022",

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doi = "10.1117/12.2626089",

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Chen, XZ, Peng, C, Li, MC & Chen, YL 2022, Vehicle Re-Identification System for Road Side Unit Application. in M Nakajima, S Muramatsu, J-G Kim, J-M Guo & Q Kemao (eds), International Workshop on Advanced Imaging Technology, IWAIT 2022., 121770V, Proceedings of SPIE - The International Society for Optical Engineering, vol. 12177, SPIE, 2022 International Workshop on Advanced Imaging Technology, IWAIT 2022, Hong Kong, China, 4/01/22. https://doi.org/10.1117/12.2626089

Vehicle Re-Identification System for Road Side Unit Application. / Chen, Xiu Zhi; Peng, Chi; Li, Mu Chuan et al.
International Workshop on Advanced Imaging Technology, IWAIT 2022. ed. / Masayuki Nakajima; Shogo Muramatsu; Jae-Gon Kim; Jing-Ming Guo; Qian Kemao. SPIE, 2022. 121770V (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 12177).

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

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T1 - Vehicle Re-Identification System for Road Side Unit Application

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AU - Chen, Yen Lin

PY - 2022

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N2 - Vehicle re-identification is one of the complex smart traffic issues, due to the large intra-class variation and high inter-class similarity, it is hard to be solved through traditional hand-crafted features. In this paper, we proposed a vehicle re-identification system based on deep learning techniques, which is able to re-identified the vehicles through deep features under acceptable operation time on Nvidia Jetson TX2. We have collected multiple sequences captured from real-world road side units (RSU) for system evaluation experiments, and the results indicate that it is highly possible to be adopted for real world traffic applications.

AB - Vehicle re-identification is one of the complex smart traffic issues, due to the large intra-class variation and high inter-class similarity, it is hard to be solved through traditional hand-crafted features. In this paper, we proposed a vehicle re-identification system based on deep learning techniques, which is able to re-identified the vehicles through deep features under acceptable operation time on Nvidia Jetson TX2. We have collected multiple sequences captured from real-world road side units (RSU) for system evaluation experiments, and the results indicate that it is highly possible to be adopted for real world traffic applications.

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KW - deep learning

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T3 - Proceedings of SPIE - The International Society for Optical Engineering

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A2 - Nakajima, Masayuki

A2 - Muramatsu, Shogo

A2 - Kim, Jae-Gon

A2 - Guo, Jing-Ming

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PB - SPIE

T2 - 2022 International Workshop on Advanced Imaging Technology, IWAIT 2022

Y2 - 4 January 2022 through 6 January 2022

ER -

Vehicle Re-Identification System for Road Side Unit Application

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Keywords

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