PECC: Position Encoding Coordinate Classification System Design for Human Pose Estimation
提出PECC系统，通过位置编码和过滤放大注意力机制改进坐标分类法，在保持低计算成本的同时提升二维人体姿态估计的精度和鲁棒性。
Coordinate classification is an efficient approach to 2-D human pose estimation (HPE), treating keypoint predictions as sub-pixel bins along horizontal and vertical axes, thereby avoiding the computationally intensive upsampling process required in traditional heatmap-based methods. In this article, we introduce the Position Encoding Coordinate Classification (PECC) system, which enhances coordinate classification by embedding position information directly into keypoint feature representations through a novel position encoding mechanism. We further design a tailored attention mechanism, Filtering Amplified Attention (FAA), optimized for coordinate classification. FAA provides finer relative positional information, improving the system’s ability to model relationships between keypoints and enhancing coordinate localization accuracy. Our method maintains the efficiency of coordinate classification by utilizing 1-D vectors, significantly reducing model parameters and computational cost. Additionally, the incorporation of positional encoding enhances the system’s ability to effectively model and exploit spatial information within a coordinate-classification-based pose estimation framework. Extensive experiments on mainstream datasets demonstrate that PECC achieves superior accuracy and robustness in 2-D HPE, advancing the state-of-the-art in this domain.