Sparse and Privacy-enhanced Representation for Human Pose Estimation
Ting-Ying Lin Lin-Yung Hsieh Fu-En Wang Wen-Shen Wuen Min Sun
We propose a sparse and privacy-enhanced representation for Human Pose
Estimation (HPE). Given a perspective camera, we use a proprietary motion
vector sensor(MVS) to extract an edge image and a two-directional motion vector
image at each time frame. Both edge and motion vector images are sparse and
contain much less information (i.e., enhancing human privacy). We advocate that
edge information is essential for HPE, and motion vectors complement edge
information during fast movements. We propose a fusion network leveraging
recent advances in sparse convolution used typically for 3D voxels to
efficiently process our proposed sparse representation, which achieves about
13x speed-up and 96% reduction in FLOPs. We collect an in-house edge and motion
vector dataset with 16 types of actions by 40 users using the proprietary MVS.
Our method outperforms individual modalities using only edge or motion vector
images. Finally, we validate the privacy-enhanced quality of our sparse
representation through face recognition on CelebA (a large face dataset) and a
user study on our in-house dataset.