research-article

Data Generation for Improving Person Re-identification

Authors:

Zhiyong GaoAuthors Info & Claims

MM '17: Proceedings of the 25th ACM international conference on Multimedia

Pages 609 - 617

https://doi.org/10.1145/3123266.3123302

Published: 19 October 2017 Publication History

Abstract

In this paper, we explore ways to address the challenges such as data bias caused by the lack of data on person re-identification problem. We propose a data generation framework from both intra- and inter-view aspects for data augmentation to advance the performance of the existing person re-identification algorithms. Specifically, for intra-view data generation, the proposed method generates useful predicted sequences within a camera view for certain person data expansion. The generated sequences well preserve the movement information of the camera and objects, which expands the original data with longer sequence length to tackle the problem caused by insufficient data from the root. For more challenging datasets which suffer from background clutters, we propose an inter-view image generation with automatic end-to-end background substitution to eliminate the influence by the background and increase the diversity of the training data as well, which makes the recognition system learn to focus on the regions of objects and image features related to identity. We then propose a flexible data augmentation method based on our data generation approaches to improve the performance of the person re-identification and analyze the advantages and applicability of these approaches respectively. Evaluated on the challenging re-id datasets, our method outperforms existing state-of-the-art approaches without any network structure modification on the baseline neural network. Cross-datasets evaluation results show that our method has favorable generalization ability and is potentially helpful for solving similar recognition tasks due to the common issue of insufficient data.

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Cited By

Jiang WZhang YZheng SLiu SYan S(2024)Data augmentation in human-centric visionVicinagearth10.1007/s44336-024-00002-91:1Online publication date: 8-Oct-2024
https://doi.org/10.1007/s44336-024-00002-9
Li YSong JNi HShen HEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Style-Controllable Generalized Person Re-identificationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611802(7912-7921)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611802
Bansal MSharma DKathuria D(2022)A Systematic Review on Data Scarcity Problem in Deep Learning: Solution and ApplicationsACM Computing Surveys10.1145/350228754:10s(1-29)Online publication date: 13-Sep-2022
https://dl.acm.org/doi/10.1145/3502287
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Index Terms

Data Generation for Improving Person Re-identification
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Graph-based database models

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Published In

cover image ACM Conferences

MM '17: Proceedings of the 25th ACM international conference on Multimedia

October 2017

2028 pages

ISBN:9781450349062

DOI:10.1145/3123266

General Chairs:
Qiong Liu
FXPAL, USA
,
Rainer Lienhart
Universität Augsburg, Germany
,
Haohong Wang
TCL America, USA
,
Program Chairs:
Sheng-Wei "Kuan-Ta" Chen
Academia Sinica, Taiwan
,
Susanne Boll
University of Oldenburg, Germany
,
Phoebe Chen
La Trobe University, Australia
,
Gerald Friedland
Lawrence Livermore National Lab, USA
,
Jia Li
Google, USA
,
Shuicheng Yan
Qihoo 360, China

Copyright © 2017 ACM.

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Published: 19 October 2017

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MM '17: ACM Multimedia Conference

October 23 - 27, 2017

California, Mountain View, USA

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MM '17 Paper Acceptance Rate 189 of 684 submissions, 28%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Jiang WZhang YZheng SLiu SYan S(2024)Data augmentation in human-centric visionVicinagearth10.1007/s44336-024-00002-91:1Online publication date: 8-Oct-2024
https://doi.org/10.1007/s44336-024-00002-9
Li YSong JNi HShen HEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Style-Controllable Generalized Person Re-identificationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611802(7912-7921)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611802
Bansal MSharma DKathuria D(2022)A Systematic Review on Data Scarcity Problem in Deep Learning: Solution and ApplicationsACM Computing Surveys10.1145/350228754:10s(1-29)Online publication date: 13-Sep-2022
https://dl.acm.org/doi/10.1145/3502287
Zheng AChen ZLi CTang JLuo B(2021)Learning Deep RGBT Representations for Robust Person Re-identificationInternational Journal of Automation and Computing10.1007/s11633-020-1262-z18:3(443-456)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s11633-020-1262-z
Zeng MTian CWu ZBoll SMu Lee KLuo JZhu WByun HWen Chen CLienhart RMei T(2018)Person Re-identification with Hierarchical Deep Learning Feature and efficient XQDA MetricProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3240717(1838-1846)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.1145/3240508.3240717
Ouyang DShao JZhang YYang YShen HBoll SMu Lee KLuo JZhu WByun HWen Chen CLienhart RMei T(2018)Video-based Person Re-identification via Self-Paced Learning and Deep Reinforcement Learning FrameworkProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3240622(1562-1570)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.1145/3240508.3240622
Liu YXie CZhou WLi H(2018)Effective Similarity Measurement for Video-based Person Re-identification2018 IEEE Visual Communications and Image Processing (VCIP)10.1109/VCIP.2018.8698685(1-4)Online publication date: Dec-2018
https://doi.org/10.1109/VCIP.2018.8698685
Liu JNi BYan YZhou PCheng SHu J(2018)Pose Transferrable Person Re-identification2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition10.1109/CVPR.2018.00431(4099-4108)Online publication date: Jun-2018
https://doi.org/10.1109/CVPR.2018.00431

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