research-article

A Large-Scale Synthetic Gait Dataset Towards in-the-Wild Simulation and Comparison Study

Authors:

Xi LiAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 1

Article No.: 17, Pages 1 - 23

https://doi.org/10.1145/3517199

Published: 05 January 2023 Publication History

Abstract

Gait recognition has a rapid development in recent years. However, current gait recognition focuses primarily on ideal laboratory scenes, leaving the gait in the wild unexplored. One of the main reasons is the difficulty of collecting in-the-wild gait datasets, which must ensure diversity of both intrinsic and extrinsic human gait factors. To remedy this problem, we propose to construct a large-scale gait dataset with the help of controllable computer simulation. In detail, to diversify the intrinsic factors of gait, we generate numerous characters with diverse attributes and associate them with various types of walking styles. To diversify the extrinsic factors of gait, we build a complicated scene with a dense camera layout. Then we design an automatic generation toolkit under Unity3D for simulating the walking scenarios and capturing the gait data. As a result, we obtain a dataset simulating towards the in-the-wild scenario, called VersatileGait, which has more than one million silhouette sequences of 10,000 subjects with diverse scenarios. VersatileGait possesses several nice properties, including huge dataset size, diverse pedestrian attributes, complicated camera layout, high-quality annotations, small domain gap with the real one, good scalability for new demands, and no privacy issues. By conducting a series of experiments, we first explore the effects of different factors on gait recognition. We further illustrate the effectiveness of using our dataset to pre-train models, which obtain considerable performance gain on CASIA-B, OU-MVLP, and CASIA-E. Besides, we show the great potential of the fine-grained labels other than the ID label in improving the efficiency and effectiveness of models. Our dataset and its corresponding generation toolkit are available at https://github.com/peterzpy/VersatileGait.

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Li TMa WZheng YFan XYang GWang LLi Z(2024)A survey on gait recognition against occlusion: taxonomy, dataset and methodologyPeerJ Computer Science10.7717/peerj-cs.260210(e2602)Online publication date: 23-Dec-2024
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Index Terms

A Large-Scale Synthetic Gait Dataset Towards in-the-Wild Simulation and Comparison Study
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Biometrics
2. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia databases

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

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 19, Issue 1

January 2023

505 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3572858

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 05 January 2023

Online AM: 21 July 2022

Accepted: 07 February 2022

Revised: 21 January 2022

Received: 01 August 2021

Published in TOMM Volume 19, Issue 1

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Zhejiang Provincial Natural Science Foundation of China
National Key Research and Development Program of China
National Natural Science Foundation of China
Zhejiang University K.P.Chao’s High Technology Development Foundation

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https://doi.org/10.7717/peerj-cs.2602
Bowen DHaiquan WYuxuan LZhao JMa YRunhe H(2024)Fair and Robust Federated Learning via Decentralized and Adaptive Aggregation based on BlockchainACM Transactions on Sensor Networks10.1145/3673656Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3673656
Ma JZhang FJin BSu CLi SWang ZNi J(2024)Push the Limit of Highly Accurate Ranging on Commercial UWB DevicesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596028:2(1-27)Online publication date: 15-May-2024
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