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Refining Crowd Pose Annotation Dataset for Accurate Multiple-Person Pose Estimations in Crowd Situations

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(12), pp.129-138
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 11, 2024
  • Accepted : November 27, 2024
  • Published : December 31, 2024

Jin-Woo Cha 1 Chulyoung Kim 2 Hyun-Jong Oh 2 Da-Jeong Seo 2 Jong-Seong Park 2 Yoo-Sung Kim 2

1인하공업전문대학
2인하대학교

Accredited

ABSTRACT

This paper describes refinement and release of a human pose annotation dataset, which is essential for developing multi-person pose estimators in crowd situations. To achieve this, we first developed an annotation refining tool that can be helpful to put accurate bounding box labels for many people at most and correct keypoint positions for each person in crowd situation images where many occlusions might occur since the crowd density is high. Using this tool, we enhanced the quality of the ground truth annotations for 8,000 test images from the CrowdPose dataset, which is widely used for evaluating the performance of multi-person pose estimators in crowd situations. Our analysis confirms that the performance of the multi-person pose estimator can be more accurately assessed, as the modified dataset contains more person’s bounding boxes and more accurate keypoint labels for each person than the previous version. The developed human pose annotation refining tool and the modified dataset will be publicly available at https://github.com/InhaKMS/HuPo-AnT.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.