Publication
Title
Human motion prediction on the IKEA-ASM dataset
Author
Abstract
Motion prediction of the human pose estimates future poses based on the preceding poses. It is a stepping stone toward industrial applications, like human-robot interactions and ergonomy indicators. The goal is to minimize the error in predicted joint positions on the IKEA-ASM dataset which resembles assembly use cases with a high diversity of execution and background of the same action class. In this paper, we use the STS- GCN model to tackle 2D motion prediction and make various alterations to improve the performance of the model. First, we pre-processed the training dataset through filtering to remove outliers and inconsistencies to boost performance by 31%. Secondly, we added object gaze information to give more context to the body motion of the subject, which lowers the error (MPJPE) to 10.1618 compared to 18.3462 without object gaze information. The increased performance indicates that there is a correlation between the object gaze and body motion. Lastly, the over-smoothing of the Graph Convolutional Network embeddings is decreased by limiting the number of layers, providing richer joint embeddings.
Language
English
Source (book)
Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 19-21 February, 2023, Lisbon, Portugal
Publication
SciTePress , 2023
ISBN
978-989-758-634-7
DOI
10.5220/0011902300003417
Volume/pages
p. 906-914
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identifier
Creation 24.11.2023
Last edited 25.11.2023
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