Self-Occluded Human Pose Recovery in Monocular Video Motion Capture

Malekian, Leila and Lapeer, Rudy (2024) Self-Occluded Human Pose Recovery in Monocular Video Motion Capture. In: 2024 14th International Conference on Pattern Recognition Systems, ICPRS 2024. 2024 14th International Conference on Pattern Recognition Systems, ICPRS 2024 . The Institute of Electrical and Electronics Engineers (IEEE), GBR. ISBN 9798350375657

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Abstract

Monocular video motion capture is a popular alternative to more expensive technologies such as marker-based optical motion capture. However, motions that are occluded from the single camera view, for example, due to self-occlusion, are difficult to recover. In this paper, we propose a machine learning-based method that is used in post-processing to reconstruct the incorrect motions that are caused by self-occlusion. The post-processing network is trained on a dataset acquired from three subjects doing 30 different basic exercise motions that include self-occlusion. The collected data comprise single video camera footage and optical motion capture data as the ground truth. To correctly reconstruct the occluded motion, action recognition information is used to select a machine learning model that is trained on the specific motion. The performance of predictive and non-predictive networks are compared to each other and also with the state of the art in human motion estimation. The results show a significant reduction of the overall pose error and the pose error for selected body parts with a large degree of self-occlusion.

Item Type:	Book Section
Additional Information:	Publisher Copyright: © 2024IEEE.
Uncontrolled Keywords:	deep learning,human pose estimation,machine learning,self-occlusion,single view video,smpl model,artificial intelligence,computer science applications,computer vision and pattern recognition,modelling and simulation ,/dk/atira/pure/subjectarea/asjc/1700/1702
Faculty \ School:	Faculty of Science Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Visual Computing and Signal Processing Faculty of Science > Research Groups > Health Technologies Faculty of Science > Research Groups > FORT-iNET
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	03 Mar 2025 16:30
Last Modified:	03 Mar 2025 16:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/98646
DOI:	10.1109/ICPRS62101.2024.10677815

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