PoseNet++ inference speed in i9-10920X.

<div><p>Human pose estimation (HPE) has made significant progress with deep learning; however, it still faces challenges in handling occlusions, complex poses, and complex multi-person scenarios. To address these issues, we propose PoseNet++, a novel approach based on a 3-stacked hourgla...

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Bibliographic Details
Main Author:	Chao Lv (335727) (author)
Other Authors:	Geyao Ma (21601507) (author)
Published:	2025
Subjects:	Biochemistry Neuroscience Biotechnology Sociology Biological Sciences not elsewhere classified Information Systems not elsewhere classified still faces challenges stacked hourglass network stacked hourglass architecture range channel dependencies novel approach based network &# 8217 mspahm enhances long made significant progress feature extraction capabilities convolutional kernel selection 1 %, respectively propose posenet ++, posenet ++&# 8201 mspahm ), coordinate mpii validation set posenet ++: thus enhancing receptive field recent years point operations person scenarios pckh score pbrm ). much lower limb joints handling occlusions deep learning crowdpose datasets cpca ), complex poses art performance
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PoseNet++ inference speed in i9-10920X.

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