PCA-Based robust motion data recovery
Authors: Li, Z., Yu, H., Kieu, H.D., Vuong, T.L. and Zhang, J.J.
Journal: IEEE Access
Volume: 8
Pages: 76980-76990
eISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.2989744
Abstract:Human motion tracking is a prevalent technique in many fields. A common difficulty encountered in motion tracking is the corrupted data is caused by detachment of markers in 3D motion data or occlusion in 2D tracking data. Most methods for missing markers problem may quickly become ineffective when gaps exist in the trajectories of multiple markers for an extended duration. In this paper, we propose the principal component eigenspace based gap filling methods that leverage a training sample set for estimation. The proposed method is especially beneficial in the scenario of motion data with less predictable or repeated movement patterns, and that of even missing entire frames within an interval of a sequence. To highlight algorithm robustness, we perform algorithms on twenty test samples for comparison. The experimental results show that our methods are numerical stable and fast to work.
https://eprints.bournemouth.ac.uk/33917/
Source: Scopus
PCA Based Robust Motion Data Recovery
Authors: Yu, H., Zhang, J.J. and Li, Z.
Journal: IEEE Access
Publisher: IEEE
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.2989744
Abstract:Human motion tracking is a prevalent technique in many fields. A common difficulty encountered in motion tracking is the corrupted data is caused by detachment of markers in 3D motion data or occlusion in 2D tracking data. Most methods for missing markers problem may quickly become ineffective when gaps exist in the trajectories of multiple markers for an extended duration. In this paper, we propose the principal component eigenspace based gap filling methods that leverage a training sample set for estimation. The proposed method is especially beneficial in the scenario of motion data with less predictable or repeated movement patterns, and that of even missing entire frames within an interval of a sequence. To highlight algorithm robustness, we perform algorithms on twenty test samples for comparison. The experimental results show that our methods are numerical stable and fast to work.
https://eprints.bournemouth.ac.uk/33917/
https://ieeexplore.ieee.org/document/9076621
Source: Manual
PCA Based Robust Motion Data Recovery.
Authors: Li, Z., Yu, H., Kieu, H.D., Vuong, T.L. and Zhang, J.J.
Journal: IEEE Access
Volume: 8
Pages: 76980-76990
ISSN: 2169-3536
Abstract:Human motion tracking is a prevalent technique in many fields. A common difficulty encountered in motion tracking is the corrupted data is caused by detachment of markers in 3D motion data or occlusion in 2D tracking data. Most methods for missing markers problem may quickly become ineffective when gaps exist in the trajectories of multiple markers for an extended duration. In this paper, we propose the principal component eigenspace based gap filling methods that leverage a training sample set for estimation. The proposed method is especially beneficial in the scenario of motion data with less predictable or repeated movement patterns, and that of even missing entire frames within an interval of a sequence. To highlight algorithm robustness, we perform algorithms on twenty test samples for comparison. The experimental results show that our methods are numerical stable and fast to work.
https://eprints.bournemouth.ac.uk/33917/
Source: BURO EPrints