Multi-View Skeleton Analysis for Human Action Segmentation Tasks

Laura Romeo, Cosimo Patruno, Grazia Cicirelli, Tiziana D’Orazio

2025

Abstract

Human Action Recognition and Segmentation have been attracting considerable attention from the scientific community in the last decades. In literature, various types of data are used for human monitoring, each with its advantages and challenges, such as RGB, IR, RGBD, and Skeleton data. Skeleton data abstracts away detailed appearance information, focusing instead on the spatial configuration of body joints and their temporal dynamics. Moreover, Skeleton representation can be robust to changes in appearance and viewpoint, making it useful for action segmentation. In this paper, we focus on the use of Skeleton data for human action segmentation in a manufacturing context by using a multi-camera system composed of two Azure Kinect cameras. This work aims to investigate action segmentation performance by using projected skeletons or “synthetic” ones. When one of the cameras fails to provide skeleton data due to occlusion or being out of range, the information coming from the other view is used to fill the missing skeletons. Furthermore, synthetic skeletons are generated from the combination of the two skeletons by considering also the reliability of each joint. Experiments on the HARMA dataset demonstrate the effects of the skeleton combinations on human action segmentation.

Paper Citation

in Harvard Style

Romeo L., Patruno C., Cicirelli G. and D’Orazio T. (2025). Multi-View Skeleton Analysis for Human Action Segmentation Tasks. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 579-586. DOI: 10.5220/0013129500003905

in Bibtex Style

@conference{icpram25,
author={Laura Romeo and Cosimo Patruno and Grazia Cicirelli and Tiziana D’Orazio},
title={Multi-View Skeleton Analysis for Human Action Segmentation Tasks},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={579-586},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013129500003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Multi-View Skeleton Analysis for Human Action Segmentation Tasks
SN - 978-989-758-730-6
AU - Romeo L.
AU - Patruno C.
AU - Cicirelli G.
AU - D’Orazio T.
PY - 2025
SP - 579
EP - 586
DO - 10.5220/0013129500003905
PB - SciTePress