Skip to main content

PCA Based Robust Motion Data Recovery.

Li, Z., Yu, H., Kieu, H.D., Vuong, T.L. and Zhang, J. J., 2020. PCA Based Robust Motion Data Recovery. IEEE Access, 8, 76980-76990.

Full text available as:

09076621.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


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.

Item Type:Article
Uncontrolled Keywords:missing marker problem; MoCap data; 2D tracking data; principle component analysis
Group:Faculty of Media & Communication
ID Code:33917
Deposited By: Symplectic RT2
Deposited On:27 Apr 2020 14:06
Last Modified:14 Mar 2022 14:21


Downloads per month over past year

More statistics for this item...
Repository Staff Only -