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Anonymization for Skeleton Action Recognition. (arXiv:2111.15129v2 [cs.CV] UPDATED)
May 30, 2022, 1:12 a.m. | Saemi Moon, Myeonghyeon Kim, Zhenyue Qin, Yang Liu, Dongwoo Kim
cs.CV updates on arXiv.org arxiv.org
Skeleton-based action recognition attracts practitioners and researchers due
to the lightweight, compact nature of datasets. Compared with RGB-video-based
action recognition, skeleton-based action recognition is a safer way to protect
the privacy of subjects while having competitive recognition performance.
However, due to improvements in skeleton estimation algorithms as well as
motion- and depth-sensors, more details of motion characteristics can be
preserved in the skeleton dataset, leading to potential privacy leakage. To
investigate the potential privacy leakage from skeleton datasets, we first …
More from arxiv.org / cs.CV updates on arXiv.org
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