PCA-Based robust motion data recovery

Authors: Yu, H., Zhang, J.J. and Li, Z.

http://eprints.bournemouth.ac.uk/33917/

https://ieeexplore.ieee.org/document/9076621

Journal: IEEE Access

Publisher: IEEE

ISSN: 2169-3536

DOI: 10.1109/ACCESS.2020.2989744

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.

This data was imported from Scopus:

Authors: Li, Z., Yu, H., Kieu, H.D., Vuong, T.L. and Zhang, J.J.

http://eprints.bournemouth.ac.uk/33917/

Journal: IEEE Access

Volume: 8

Pages: 76980-76990

eISSN: 2169-3536

DOI: 10.1109/ACCESS.2020.2989744

© 2013 IEEE. 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.

The data on this page was last updated at 05:24 on October 24, 2020.