JRDB-Pose: A large-scale dataset for multi-person pose estimation and tracking

Edward Vendrow, Duy Tho Le, Jianfei Cai, Hamid Rezatofighi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Autonomous robotic systems operating in human environments must understand their surroundings to make accurate and safe decisions. In crowded human scenes with close-up human-robot interaction and robot navigation, a deep understanding of surrounding people requires reasoning about human motion and body dynamics over time with human body pose estimation and tracking. However, existing datasets captured from robot platforms either do not provide pose annotations or do not reflect the scene distribution of social robots. In this paper, we introduce JRDB-Pose, a large-scale dataset and benchmark for multi-person pose estimation and tracking. JRDB-Pose extends the existing JRDB which includes videos captured from a social navigation robot in a university campus environment, containing challenging scenes with crowded indoor and outdoor locations and a diverse range of scales and occlusion types. JRDB-Pose provides human pose annotations with per-keypoint occlusion labels and track IDs consistent across the scene and with existing annotations in JRDB. We conduct a thorough experimental study of state-of-the-art multi-person pose estimation and tracking methods on JRDB-Pose, showing that our dataset imposes new challenges for the existing methods. JRDB-Pose is available at https://jrdb.erc.monash.edu/.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023
EditorsEric Mortensen
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages4811-4820
Number of pages10
ISBN (Electronic)9798350301298
ISBN (Print)9798350301304
DOIs
Publication statusPublished - 2023
EventIEEE Conference on Computer Vision and Pattern Recognition 2023 - Vancouver, Canada
Duration: 18 Jun 202322 Jun 2023
https://cvpr2023.thecvf.com/ (Website)
https://openaccess.thecvf.com/CVPR2023?day=all (Proceedings)
https://ieeexplore.ieee.org/xpl/conhome/10203037/proceeding (Proceedings)
https://cvpr2023.thecvf.com/Conferences/2023 (Website)

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2023
Abbreviated titleCVPR 2023
Country/TerritoryCanada
CityVancouver
Period18/06/2322/06/23
Internet address

Keywords

  • body
  • gesture
  • Humans: Face
  • movement
  • pose

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