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Correlation Discrepancy Insight Network for Video Re-identification

Authors:

Jiayi MaAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 16, Issue 4

Article No.: 120, Pages 1 - 21

https://doi.org/10.1145/3402666

Published: 17 December 2020 Publication History

Abstract

Video-based person re-identification (ReID) aims at re-identifying a specified person sequence from videos that were captured by disjoint cameras. Most existing works on this task ignore the quality discrepancy across frames by using all video frames to develop a ReID method. Additionally, they adopt only the person self-characteristic as the representation, which cannot adapt to cross-camera variation effectively. To that end, we propose a novel correlation discrepancy insight network for video-based person ReID, which consists of an unsupervised correlation insight model (CIM) for video purification and a discrepancy description network (DDN) for person representation. Concretely, CIM is constructed by using kernelized correlation filters to encode person half-parts, which evaluates the frame quality by the cross correlation across frames for selecting discriminative video fragments. Furthermore, DDN exploits the selected video fragments to generate a discrepancy descriptor using a compression network, which aims at employing the discrepancies with other persons’ to facilitate the representation of the target person rather than only using the self-characteristic. Due to the advantage in handling cross-domain variation, the discrepancy descriptor is expected to provide a new pattern for the object representation in cross-camera tasks. Experimental results on three public benchmarks demonstrate that the proposed method outperforms several state-of-the-art methods.

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Chen HWang QRuan WZhu JLei LWu XHao G(2023)ALFPNInternational Journal of Intelligent Systems10.1155/2023/62662092023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/6266209
He QZheng ZHu H(2023)A Feature Map is Worth a Video Frame: Rethinking Convolutional Features for Visible-Infrared Person Re-identificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/361737520:2(1-20)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1145/3617375
Wang KDing CPang JXu X(2023)Context Sensing Attention Network for Video-based Person Re-identificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/357320319:4(1-20)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3573203
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Index Terms

Correlation Discrepancy Insight Network for Video Re-identification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object identification

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 16, Issue 4

November 2020

372 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3444749

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2020 ACM.

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Publication History

Published: 17 December 2020

Accepted: 01 May 2020

Revised: 01 May 2020

Received: 01 September 2019

Published in TOMM Volume 16, Issue 4

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National Key R&D Program of China
National Nature Science Foundation of China
Natural Science Fundation of Hubei Province

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Cited By

Chen HWang QRuan WZhu JLei LWu XHao G(2023)ALFPNInternational Journal of Intelligent Systems10.1155/2023/62662092023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/6266209
He QZheng ZHu H(2023)A Feature Map is Worth a Video Frame: Rethinking Convolutional Features for Visible-Infrared Person Re-identificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/361737520:2(1-20)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1145/3617375
Wang KDing CPang JXu X(2023)Context Sensing Attention Network for Video-based Person Re-identificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/357320319:4(1-20)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3573203
Zhang XXie XLai JZheng W(2023)Cross-Camera Trajectories Help Person Retrieval in a Camera NetworkIEEE Transactions on Image Processing10.1109/TIP.2023.329051532(3806-3820)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIP.2023.3290515
Leng JWang HGao XZhang YWang YMo M(2023)Where to look: Multi-granularity occlusion aware for video person re-identificationNeurocomputing10.1016/j.neucom.2023.03.003536(137-151)Online publication date: Jun-2023
https://doi.org/10.1016/j.neucom.2023.03.003
Wu JChang KLin I(2023)Generalizable person re-identification with part-based multi-scale networkMultimedia Tools and Applications10.1007/s11042-023-14718-182:25(38639-38666)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s11042-023-14718-1
She LYou MWang JZeng Y(2022)Video-based Person re-identification with parallel correction and fusion of pedestrian area featuresMathematical Biosciences and Engineering10.3934/mbe.202316420:2(3504-3527)Online publication date: 2022
https://doi.org/10.3934/mbe.2023164
Zheng YZhou YZhao JChen YYao RLiu BSaddik A(2022)Clustering Matters: Sphere Feature for Fully Unsupervised Person Re-identificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/350140418:4(1-18)Online publication date: 15-Mar-2022
https://dl.acm.org/doi/10.1145/3501404
Ruan WYe MWu YLiu WChen JLiang CLi GLin C(2022)TICNet: A Target-Insight Correlation Network for Object TrackingIEEE Transactions on Cybernetics10.1109/TCYB.2021.307067752:11(12150-12162)Online publication date: Nov-2022
https://doi.org/10.1109/TCYB.2021.3070677
Bai SMa BChang HHuang RShan SChen X(2022)SANet: Statistic Attention Network for Video-Based Person Re-IdentificationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2021.311998332:6(3866-3879)Online publication date: Jun-2022
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