SAHF-LightPoseResNet: Spatially-Aware Attention-Based Hierarchical Features Enabled Lightweight PoseResNet for 2D Human Pose Estimation

In recent years, 2D human pose estimation (HPE) has become increasingly important in complex computer vision tasks, including understanding human behavior and interaction. Despite challenges like occlusion, unfavorable lighting, and motion blur, deep learning techniques have revolutionized 2D HPE by allowing automatic feature learning from data and improving generalization. We proposed a new model called Spatially-aware Attention-based Hierarchical Features Enabled Lightweight PoseResNet (SAHF-LightPoseResNet) for 2D HPE. This model extends the simple baseline network by using Spatially-aware Attention-based Hierarchical Features to enhance accuracy while minimizing parameters. The proposed model efficiently captures finer details by incorporating ResNet18, Global Context Blocks, and a novel SAHF module. Our SAHF-LightPoseResNet approach demonstrates superior performance compared to existing state-of-the-art methods, achieving PCKh@0.5 a of 90.8 and a Mean@0.1 metric of 41.1, highlighting its enhanced accuracy and efficiency. This model has important practical applications in robotics, gaming, and human-computer interaction, where accurate and efficient 2D HPE is essential.