Nonlocal low-rank regularization for human motion recovery based on similarity analysis

2019 ◽  
Vol 493 ◽  
pp. 57-74 ◽  
Author(s):  
Qiongjie Cui ◽  
Beijia Chen ◽  
Huaijiang Sun
2018 ◽  
Vol 27 (6) ◽  
pp. 3011-3024 ◽  
Author(s):  
Guiyu Xia ◽  
Huaijiang Sun ◽  
Beijia Chen ◽  
Qingshan Liu ◽  
Lei Feng ◽  
...  

2018 ◽  
Vol 450 ◽  
pp. 89-108 ◽  
Author(s):  
Beijia Chen ◽  
Huaijiang Sun ◽  
Guiyu Xia ◽  
Lei Feng ◽  
Bin Li

2019 ◽  
Vol 28 (2) ◽  
pp. 1023-1034 ◽  
Author(s):  
Lichen Wang ◽  
Zhengming Ding ◽  
Yun Fu

2006 ◽  
Vol 10 (2) ◽  
pp. 112-115 ◽  
Author(s):  
Masashi Uchinoumi ◽  
Joo Kooi Tan ◽  
Seiji Ishikawa ◽  
Toru Naito ◽  
Makoto Yokota

2019 ◽  
Vol 32 (14) ◽  
pp. 10127-10142
Author(s):  
Qiongjie Cui ◽  
Huaijiang Sun ◽  
Yupeng Li ◽  
Yue kong

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4525
Author(s):  
Kaveh Kamali ◽  
Ali Akbar Akbari ◽  
Christian Desrosiers ◽  
Alireza Akbarzadeh ◽  
Martin J.-D. Otis ◽  
...  

Due to occlusion or detached markers, information can often be lost while capturing human motion with optical tracking systems. Based on three natural properties of human gait movement, this study presents two different approaches to recover corrupted motion data. These properties are used to define a reconstruction model combining low-rank matrix completion of the measured data with a group-sparsity prior on the marker trajectories mapped in the frequency domain. Unlike most existing approaches, the proposed methodology is fully unsupervised and does not need training data or kinematic information of the user. We evaluated our methods on four different gait datasets with various gap lengths and compared their performance with a state-of-the-art approach using principal component analysis (PCA). Our results showed recovering missing data more precisely, with a reduction of at least 2 mm in mean reconstruction error compared to the literature method. When a small number of marker trajectories is available, our findings showed a reduction of more than 14 mm for the mean reconstruction error compared to the literature approach.


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