PoseAlign network for hybrid structure in 2D human pose estimation.

Area of Science:

• Computer Vision
• Machine Learning

Background:

• Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) are prominent in Human Pose Estimation (HPE).
• Hybrid approaches can leverage the strengths of both ViTs and CNNs for enhanced HPE.

Purpose of the Study:

• To introduce a novel 2D HPE method, the PoseAlign Network for Hybrid Structure (PAN-HS).
• To combine the advantages of ViTs and CNNs for improved keypoint localization precision.

Main Methods:

• Designed Spatial Align and Channel Align blocks using depth-wise convolution for feature extraction.
• Implemented an efficient Point Reposition Attention mechanism to balance local and global feature representations.
• Aggregated intra-layer features for fine-grained localized representations.

Main Results:

• Achieved a mean PCKh of 92.74% on the MPII Dataset.
• Demonstrated superior performance and enhanced keypoint localization precision in HPE tasks.
• Improved representation of mid-level spatial and channel features.

Conclusions:

• The PAN-HS method effectively integrates ViTs and CNNs for advanced Human Pose Estimation.
• The proposed attention mechanism and feature extraction blocks contribute to precise keypoint localization.
• PAN-HS offers a promising direction for future research in hybrid HPE architectures.