Discrete subspace structure constrained human motion capture data recovery

Hu, Wenyu; Zhu, Xuefang; Wang, Tinghua; Yi, Yun; Yu, Gaohang

doi:10.1016/j.asoc.2022.109617

Cited by 2 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Edwards and Green [16] conducted an analysis and found that an SMA filter with a window length of 5 frames provided the best denoising of upper limb motion data extrapolated by a markerless mocap system. [45], [14], [46], [47], [48], [27], [49] Low-Rank Matrix Completion ✓ [43], [50], [51], [52], [53] Noisy Low-Rank Matrix Completion ✓ ✓ [54], [55], [56], [52], [57], [58], [59], [60] Robust Principal Component Analysis ✓ [61], [62], [63], [64], [65], [66], [67], [68]…”

Section: A Moving Averagementioning

confidence: 99%

See 1 more Smart Citation

Denoising and Completion Filters for Human Motion Software: a Survey with Code

Martini,

Calanca,

Bombieri

2024

Preprint

View full text Add to dashboard Cite

Software platforms used for human motion analysis are increasingly being utilized in various fields, ranging from Healthcare to Industry 5.0. However, the inherent inaccuracy of these platforms often results in noisy descriptions of human poses or time periods during which the information is even missing. As a consequence, data filtering for denoising or completion is a common yet fundamental step before data analysis. Over the years, different techniques have been proposed, from generalpurpose solutions based on low-pass filters to more advanced and embedded approaches based on state observers rather than deep learning. This survey presents the current state-of-the-art filtering solutions for denoising and completing data generated by software platforms for human motion analysis, with a focus on 3D positional data extrapolated through marker-based or markerless motion capture systems. It proposes a concise taxonomy based on the filter technology and application assumptions. For each class, it summarizes the basic concepts and reports application feedback collected from literature. The survey also includes implementation codes or links to the authors’ original codes, allowing readers to easily reproduce all the algorithms in different experimental settings (https://github.com/PARCO-LAB/mocap-refinement).

show abstract

Section: A Moving Averagementioning

confidence: 99%

“…where || • || 1 is ℓ1−norm and λ is a weighting coefficient to balance the effects of the two parts. Contributions based on LRMC are [54], [55], [56], [59], [57], [52], [60].…”

Section: Noisy Low-rank Matrix Completionmentioning

confidence: 99%