Research on Human Pose Estimation Model Based on Long-Range Fine-Grained Modeling
Journal Title: Engineering and Technology Journal - Year 2024, Vol 9, Issue 04
Abstract
To address the issues of lacking long-range spatial position learning ability and excessive loss of fine-grained feature information during spatial feature pooling in human pose estimation models, a novel human pose estimation model based on the Yolopose network is proposed. Firstly, an enhanced coordinate attention module is introduced and embedded into the backbone network to endow the model with long-range spatial position modeling capability. Secondly, a fine-grained cascaded spatial pyramid pooling module is proposed to mitigate the loss of fine-grained feature information caused by spatial feature pooling. Finally, an implicit knowledge learning module is incorporated to reduce the model parameter count and enhance the model's capability for multi-task joint optimization.
Authors and Affiliations
Ziyang Lin,
Keywords
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- EP ID EP734471
- DOI 10.47191/etj/v9i04.21
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