Pevr: Pose estimation for vehicle re-identification

Saifullah Tumrani; Zhiyi Deng; Abdullah Aman Khan; Waqar Ali

doi:10.1007/978-3-030-33982-1_6

Pevr: Pose estimation for vehicle re-identification

Saifullah Tumrani^*
, Zhiyi Deng
, Abdullah Aman Khan
, Waqar Ali

^*Corresponding author for this work

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

3 Scopus citations

Abstract

Re-identification is a challenging task because the available information is partial. This paper presents an approach to tackle vehicle re-identification (Re-id) problem. We focus on pose estimation for vehicles, which is an important module of vehicle Re-id. Person Re-id received huge attention, while vehicle re-id was ignored, but recently the computer vision community have started focusing on this topic and have tried to solve this problem by only using spatiotemporal information while neglecting the driving direction. The proposed technique is using visual features to find poses of the vehicle which helps to find driving directions. Experiments are conducted on publicly available datasets VeRi and CompCars, the proposed approach got excellent results.

Original language	English
Title of host publication	Web and Big Data- APWeb-WAIM 2019 International Workshops KGMA and DSEA, Revised Selected Papers
Editors	Jingkuan Song, Xiaofeng Zhu
Publisher	Springer
Pages	69-78
Number of pages	10
ISBN (Print)	9783030339814
DOIs	https://doi.org/10.1007/978-3-030-33982-1_6
State	Published - 2019
Externally published	Yes
Event	2nd International Workshop on Knowledge Graph Management and Analysis, KGMA 2019, and 1st International Workshop on Data Science for Emerging Applications, DSEA 2019, held jointly with the 3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019 - Chengdu, China Duration: 1 Aug 2019 → 3 Aug 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11809 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Workshop on Knowledge Graph Management and Analysis, KGMA 2019, and 1st International Workshop on Data Science for Emerging Applications, DSEA 2019, held jointly with the 3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019
Country/Territory	China
City	Chengdu
Period	1/08/19 → 3/08/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

Keywords

Machine learning
Pose classifying model
Pose estimation
Vehicle re-identification

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-33982-1_6

Cite this

Tumrani, S., Deng, Z., Khan, A. A., & Ali, W. (2019). Pevr: Pose estimation for vehicle re-identification. In J. Song, & X. Zhu (Eds.), Web and Big Data- APWeb-WAIM 2019 International Workshops KGMA and DSEA, Revised Selected Papers (pp. 69-78). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11809 LNCS). Springer. https://doi.org/10.1007/978-3-030-33982-1_6

Tumrani, Saifullah ; Deng, Zhiyi ; Khan, Abdullah Aman et al. / Pevr : Pose estimation for vehicle re-identification. Web and Big Data- APWeb-WAIM 2019 International Workshops KGMA and DSEA, Revised Selected Papers. editor / Jingkuan Song ; Xiaofeng Zhu. Springer, 2019. pp. 69-78 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{a832ccceb4e8497d83735a810a94e3e4,

title = "Pevr: Pose estimation for vehicle re-identification",

abstract = "Re-identification is a challenging task because the available information is partial. This paper presents an approach to tackle vehicle re-identification (Re-id) problem. We focus on pose estimation for vehicles, which is an important module of vehicle Re-id. Person Re-id received huge attention, while vehicle re-id was ignored, but recently the computer vision community have started focusing on this topic and have tried to solve this problem by only using spatiotemporal information while neglecting the driving direction. The proposed technique is using visual features to find poses of the vehicle which helps to find driving directions. Experiments are conducted on publicly available datasets VeRi and CompCars, the proposed approach got excellent results.",

keywords = "Machine learning, Pose classifying model, Pose estimation, Vehicle re-identification",

author = "Saifullah Tumrani and Zhiyi Deng and Khan, \{Abdullah Aman\} and Waqar Ali",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 2nd International Workshop on Knowledge Graph Management and Analysis, KGMA 2019, and 1st International Workshop on Data Science for Emerging Applications, DSEA 2019, held jointly with the 3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019 ; Conference date: 01-08-2019 Through 03-08-2019",

year = "2019",

doi = "10.1007/978-3-030-33982-1\_6",

language = "English",

isbn = "9783030339814",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

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editor = "Jingkuan Song and Xiaofeng Zhu",

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Tumrani, S, Deng, Z, Khan, AA & Ali, W 2019, Pevr: Pose estimation for vehicle re-identification. in J Song & X Zhu (eds), Web and Big Data- APWeb-WAIM 2019 International Workshops KGMA and DSEA, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11809 LNCS, Springer, pp. 69-78, 2nd International Workshop on Knowledge Graph Management and Analysis, KGMA 2019, and 1st International Workshop on Data Science for Emerging Applications, DSEA 2019, held jointly with the 3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019, Chengdu, China, 1/08/19. https://doi.org/10.1007/978-3-030-33982-1_6

Pevr: Pose estimation for vehicle re-identification. / Tumrani, Saifullah; Deng, Zhiyi; Khan, Abdullah Aman et al.
Web and Big Data- APWeb-WAIM 2019 International Workshops KGMA and DSEA, Revised Selected Papers. ed. / Jingkuan Song; Xiaofeng Zhu. Springer, 2019. p. 69-78 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11809 LNCS).

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

TY - GEN

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T2 - 2nd International Workshop on Knowledge Graph Management and Analysis, KGMA 2019, and 1st International Workshop on Data Science for Emerging Applications, DSEA 2019, held jointly with the 3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019

AU - Tumrani, Saifullah

AU - Deng, Zhiyi

AU - Khan, Abdullah Aman

AU - Ali, Waqar

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - Re-identification is a challenging task because the available information is partial. This paper presents an approach to tackle vehicle re-identification (Re-id) problem. We focus on pose estimation for vehicles, which is an important module of vehicle Re-id. Person Re-id received huge attention, while vehicle re-id was ignored, but recently the computer vision community have started focusing on this topic and have tried to solve this problem by only using spatiotemporal information while neglecting the driving direction. The proposed technique is using visual features to find poses of the vehicle which helps to find driving directions. Experiments are conducted on publicly available datasets VeRi and CompCars, the proposed approach got excellent results.

AB - Re-identification is a challenging task because the available information is partial. This paper presents an approach to tackle vehicle re-identification (Re-id) problem. We focus on pose estimation for vehicles, which is an important module of vehicle Re-id. Person Re-id received huge attention, while vehicle re-id was ignored, but recently the computer vision community have started focusing on this topic and have tried to solve this problem by only using spatiotemporal information while neglecting the driving direction. The proposed technique is using visual features to find poses of the vehicle which helps to find driving directions. Experiments are conducted on publicly available datasets VeRi and CompCars, the proposed approach got excellent results.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Web and Big Data- APWeb-WAIM 2019 International Workshops KGMA and DSEA, Revised Selected Papers

A2 - Song, Jingkuan

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PB - Springer

Y2 - 1 August 2019 through 3 August 2019

ER -

Tumrani S, Deng Z, Khan AA, Ali W. Pevr: Pose estimation for vehicle re-identification. In Song J, Zhu X, editors, Web and Big Data- APWeb-WAIM 2019 International Workshops KGMA and DSEA, Revised Selected Papers. Springer. 2019. p. 69-78. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-33982-1_6

Pevr: Pose estimation for vehicle re-identification

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this