research-article

Vehicle re-identification collaborating visual and temporal-spatial network

Authors:

Ruimin HuAuthors Info & Claims

ICIMCS '13: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service

Pages 121 - 125

https://doi.org/10.1145/2499788.2499844

Published: 17 August 2013 Publication History

Abstract

Vehicle re-identification, retrieving a vehicle detected by one camera with the same vehicle by another camera, is an important problem in the video investigation application which is a technology for criminal investigation. In this task, it not only needs to classify the vehicle category, but also to identify a specific object in the category. Previous methods mainly focus on the vehicle categorization, which cannot identify the specific vehicle. In this paper, a two-stage strategy is proposed to accomplish vehicle re-identification in realistic surveillance videos. Specifically, in the first stage, a part-based appearance model fusing multiple visual features is proposed to represent the vehicle object, and then a coarse ranking list is generated by comparing appearance models of the probe and gallery vehicles. In the second stage, the temporal-spatial relation is introduced to re-rank the above visual-based ranking list, where vehicles of the same category and reasonable spatial-temporal relations are placed in top positions while those of mismatched types or relations are placed in rear positions. Both quantitative and qualitative experiments conducted on a real world dataset have validated the effectiveness of the proposed method.

References

[1]

Zehang Sun, ETreppid Technol. 2006. On-road vehicle detection: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence.29,5(May 2006),694--711. DOI= http://dx.doi.org/10.1109/TPAMI.2006.104.

Digital Library

[2]

David G. Lowe. 2004. Distinctive Image Features from Scale-Invariant Key points. International Journal of Computer Vision. 60, 2 (Nov. 2004), 91--110. DOI= http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94.

Digital Library

[3]

Alexander C. Berg, Jitendra Malik. 2001. Geometric Blur for Template Matching. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1(2001), 607--614. DOI=http://dx.doi.org/10.1109/CVPR.2001.990529.

[4]

Jianguo Zhang, M. Marszalek. 2006. Local Features and Kernels for Classification of Texture and Object Categories:A Comprehensive Study. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshop. (June 2006). DOI=http://dx.doi.org/10.1109/CVPRW.2006.121.

Digital Library

[5]

S. Lazebnik, C. Schmid, and J. Ponce. 2006. Beyond Bags of Features: Spatial Pyramid Match for Recognizing Natural Scene Categories. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2(June 2006), 2169--2178. DOI=http://dx.doi.org/10.1109/CVPR.2006.68.

Digital Library

[6]

Xiaoyu Wang, Tony X. Han, and Shuicheng Yan. 2009. An HOG-LBP human detector with partial occlusion handling. In Proceedings of the 2009 IEEE International Conference on Computer Vision, (Sept. 2009), 32--39. DOI=http://dx.doi.org/10.1109/ICCV.2009.5459207.

[7]

Dalal, Navneet, and Bill Triggs. Histograms of oriented gradients for human detection. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.1,(June 2005),886--893. DOI=10.1109/CVPR.2005.117.

Digital Library

[8]

Feris, R., Siddiquie, B., Zhai, Y., Petterson. 2011. Attribute-based vehicle search in crowded surveillance videos. In Proceedings of the 2011 ACM International Conference on Multimedia Retrieval. 18(Apr. 2011), 18--26. DOI=http://doi.acm.org/10.1145/1991996.1992014.

Digital Library

[9]

H. Moon, R. Chellappa, and A. Rosenfeld. 2002. Optimal Edge-Based Shape Detection. IEEE Transactions On Image Processing, 11(Nov. 2002), 1209--1227. DOI=http://dx.doi.org/10.1109/TIP.2002.800896.

Digital Library

[10]

Davis J V, Kulis B, Jain P. 2007. Information-theoretic metric learning. In Proceedings of the 2007 ACM International Conference on Machine Learning. (Jun. 2007), 209--216. DOI=http://doi.acm.org/10.1145/1273496.1273523.

Digital Library

[11]

Bao S. Y., Xiang Y., Savarese S. 2012. Object Co-detection. In Proceedings of the 12^th European Conference on Computer Vision. 7572 (Oct. 2012), 86--101. DOI=http://dx.doi.org/10.1007/987-3-642-33718-5_7.

Digital Library

[12]

O'Leary M B, Cummings J N. 2007. The spatial, temporal, and configurational characteristics of geographic dispersion in teams. Journal of Management Information Systems Quarterly. 31,3(2007), 433--452.

Digital Library

[13]

Monclar R, Tecla A, Oliveira J, et al. 2009. MEK: Using spatial--temporal information to improve social networks and knowledge dissemination. Journal of Information Sciences, 179,15(2009), 2524--2537. DOI=http://dx.doi.org/10.1016/j.ins.2009.01.032.

Digital Library

[14]

Felzenszwalb, P. F., Girshick, R. B., McAllester, D., Ramanan, D. 2010. Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32, 9(2010), 1627--1645. DOI=http://dx.doi.org/10.1109/TPAMI.2009.167.

Digital Library

[15]

Piccardi, Massimo. 2004. Background subtraction techniques: a review. In Proceedings of IEEE International Conference on System, Man, and Cybernetics -- SMC. 4(2004), 3099--3104. DOI=http://dx.doi.org/10.1109/ICSMC.2004.1400815.

[16]

Zivkovic, Z. Improved adaptive Gaussian mixture model for background subtraction. In Proceedings of the 17^th International Conference on Pattern Recognition. 2(2004), 28--31. DOI= http://dx.doi.org/10.1109/ICPR.2004.1333992.

Digital Library

Cited By

Zhao YShen CWang HChen S(2020)Structural Analysis of Attributes for Vehicle Re-Identification and RetrievalIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.289627321:2(723-734)Online publication date: Feb-2020
https://doi.org/10.1109/TITS.2019.2896273
Peng JHao YXu FFu X(2020)Vehicle re-identification using multi-task deep learning network and spatio-temporal modelMultimedia Tools and Applications10.1007/s11042-020-09356-wOnline publication date: 29-Aug-2020
https://doi.org/10.1007/s11042-020-09356-w
Hsieh JChen H(2018)Real-Time Vehicle Re-Identification System Using Symmelets and Deep PatchMatch Nets2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00398(2317-2322)Online publication date: Oct-2018
https://doi.org/10.1109/SMC.2018.00398
Show More Cited By

Index Terms

Vehicle re-identification collaborating visual and temporal-spatial network
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

cover image ACM Other conferences

ICIMCS '13: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service

August 2013

419 pages

ISBN:9781450322522

DOI:10.1145/2499788

Conference Chair:
Tat-Seng Chua
National University of Singapore, Singapore
,
General Chairs:
Ke Lu
University of Chinese Academy of Sciences, China
,
Tao Mei
Microsoft Research Asia, China
,
Xindong Wu
Hefei University of Technology, China

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

NSF of China: National Natural Science Foundation of China
University of Sciences & Technology, Hefei: University of Sciences & Technology, Hefei
Beijing ACM SIGMM Chapter

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 August 2013

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Funding Sources

science and technology Program of Wuhan
Ministry of Science and Technology of the People's Republic of China
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
major national science and technology special projects
Natural Science Foundation of Hubei Province

Conference

ICIMCS '13

Sponsor:

NSF of China
University of Sciences & Technology, Hefei

ICIMCS '13: International Conference on Internet Multimedia Computing and Service

August 17 - 19, 2013

Huangshan, China

Acceptance Rates

ICIMCS '13 Paper Acceptance Rate 20 of 94 submissions, 21%;

Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

Zhao YShen CWang HChen S(2020)Structural Analysis of Attributes for Vehicle Re-Identification and RetrievalIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.289627321:2(723-734)Online publication date: Feb-2020
https://doi.org/10.1109/TITS.2019.2896273
Peng JHao YXu FFu X(2020)Vehicle re-identification using multi-task deep learning network and spatio-temporal modelMultimedia Tools and Applications10.1007/s11042-020-09356-wOnline publication date: 29-Aug-2020
https://doi.org/10.1007/s11042-020-09356-w
Hsieh JChen H(2018)Real-Time Vehicle Re-Identification System Using Symmelets and Deep PatchMatch Nets2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00398(2317-2322)Online publication date: Oct-2018
https://doi.org/10.1109/SMC.2018.00398
Chen HHsieh JHuang S(2018)Real-Time Vehicle Re-Identification System Using Symmelets and HOMs2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)10.1109/AVSS.2018.8639390(1-6)Online publication date: Nov-2018
https://doi.org/10.1109/AVSS.2018.8639390

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