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Dense 3D-Convolutional Neural Network for Person Re-Identification in Videos

Authors:

Yongdong ZhangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 15, Issue 1s

Article No.: 8, Pages 1 - 19

https://doi.org/10.1145/3231741

Published: 24 January 2019 Publication History

Abstract

Person re-identification aims at identifying a certain pedestrian across non-overlapping multi-camera networks in different time and places. Existing person re-identification approaches mainly focus on matching pedestrians on images; however, little attention has been paid to re-identify pedestrians in videos. Compared to images, video clips contain motion patterns of pedestrians, which is crucial to person re-identification. Moreover, consecutive video frames present pedestrian appearance with different body poses and from different viewpoints, providing valuable information toward addressing the challenge of pose variation, occlusion, and viewpoint change, and so on. In this article, we propose a Dense 3D-Convolutional Network (D3DNet) to jointly learn spatio-temporal and appearance representation for person re-identification in videos. The D3DNet consists of multiple three-dimensional (3D) dense blocks and transition layers. The 3D dense blocks enlarge the receptive fields of visual neurons in both spatial and temporal dimensions, leading to discriminative appearance representation as well as short-term and long-term motion patterns of pedestrians without the requirement of an additional motion estimation module. Moreover, we formulate a loss function consisting of an identification loss and a center loss to minimize intra-class variance and maximize inter-class variance simultaneously, toward addressing the challenge of large intra-class variance and small inter-class variance. Extensive experiments on two real-world video datasets of person identification, i.e., MARS and iLIDS-VID, have shown the effectiveness of the proposed approach.

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Cited By

Yang XWang XLiu LWang NGao X(2024)STFE: A Comprehensive Video-Based Person Re-Identification Network Based on Spatio-Temporal Feature EnhancementIEEE Transactions on Multimedia10.1109/TMM.2024.336213626(7237-7249)Online publication date: 2024
https://doi.org/10.1109/TMM.2024.3362136
Liu XZhang PYu CQian XYang XLu H(2024)A Video Is Worth Three Views: Trigeminal Transformers for Video-Based Person Re-IdentificationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.338691425:9(12818-12828)Online publication date: Sep-2024
https://doi.org/10.1109/TITS.2024.3386914
Chen SDa HWang DZhang XYan YZhu S(2024)HASI: Hierarchical Attention-Aware Spatio–Temporal Interaction for Video-Based Person Re-IdentificationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.334042834:6(4973-4988)Online publication date: Jun-2024
https://doi.org/10.1109/TCSVT.2023.3340428
Show More Cited By

Index Terms

Dense 3D-Convolutional Neural Network for Person Re-Identification in Videos
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object identification
      2. Computer vision tasks
        Visual content-based indexing and retrieval
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

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

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 15, Issue 1s

Special Section on Deep Learning for Intelligent Multimedia Analytics and Special Section on Multi-Modal Understanding of Social, Affective and Subjective Attributes of Data

January 2019

265 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3309769

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2019 ACM.

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Association for Computing Machinery

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Publication History

Published: 24 January 2019

Accepted: 01 June 2018

Revised: 01 April 2018

Received: 01 October 2017

Published in TOMM Volume 15, Issue 1s

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National Key R8D Program of China
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Cited By

Yang XWang XLiu LWang NGao X(2024)STFE: A Comprehensive Video-Based Person Re-Identification Network Based on Spatio-Temporal Feature EnhancementIEEE Transactions on Multimedia10.1109/TMM.2024.336213626(7237-7249)Online publication date: 2024
https://doi.org/10.1109/TMM.2024.3362136
Liu XZhang PYu CQian XYang XLu H(2024)A Video Is Worth Three Views: Trigeminal Transformers for Video-Based Person Re-IdentificationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.338691425:9(12818-12828)Online publication date: Sep-2024
https://doi.org/10.1109/TITS.2024.3386914
Chen SDa HWang DZhang XYan YZhu S(2024)HASI: Hierarchical Attention-Aware Spatio–Temporal Interaction for Video-Based Person Re-IdentificationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.334042834:6(4973-4988)Online publication date: Jun-2024
https://doi.org/10.1109/TCSVT.2023.3340428
Liu LYang XWang NGao X(2023)Frequency Information Disentanglement Network for Video-Based Person Re-IdentificationIEEE Transactions on Image Processing10.1109/TIP.2023.329690132(4287-4298)Online publication date: 2023
https://doi.org/10.1109/TIP.2023.3296901
Yadav AVishwakarma D(2023)Deep learning algorithms for person re-identification: sate-of-the-art and research challengesMultimedia Tools and Applications10.1007/s11042-023-16286-w83:8(22005-22054)Online publication date: 10-Aug-2023
https://doi.org/10.1007/s11042-023-16286-w
Wu WLiu J(2022)Self-supervised human semantic parsing for video-based person re-identificationJUSTC10.52396/JUSTC-2021-021252:9(5)Online publication date: 2022
https://doi.org/10.52396/JUSTC-2021-0212
Trabelsi RKhemmar RDecoux BErtaud JButteau R(2022)Recent Advances in Vision-Based On-Road Behaviors Understanding: A Critical SurveySensors10.3390/s2207265422:7(2654)Online publication date: 30-Mar-2022
https://doi.org/10.3390/s22072654
Harris EKhoo IDemircan E(2022)A Survey of Human Gait-Based Artificial Intelligence ApplicationsFrontiers in Robotics and AI10.3389/frobt.2021.7492748Online publication date: 3-Jan-2022
https://doi.org/10.3389/frobt.2021.749274
Liu TZhu CYang L(2022)Efficient Text-based Person Search via Single-stage Identity-guided Attribute Parsing and Alignment2022 26th International Conference on Pattern Recognition (ICPR)10.1109/ICPR56361.2022.9956569(4111-4117)Online publication date: 21-Aug-2022
https://doi.org/10.1109/ICPR56361.2022.9956569
Wu WLiu JZheng KSun QZha Z(2022)Temporal Complementarity-Guided Reinforcement Learning for Image-to-Video Person Re-Identification2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52688.2022.00717(7309-7318)Online publication date: Jun-2022
https://doi.org/10.1109/CVPR52688.2022.00717
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