Full Scale-Aware Balanced High-Resolution Network for Multi-Person Pose Estimation

Scale variation is a major challenge in multi-person pose estimation. In scenes where persons are present at various distances, models tend to perform better on larger-scale persons, while the performance for smaller-scale persons often falls short of expectations. Therefore, effectively balancing the persons of different scales poses a significant challenge. So this paper proposes a new multi-person pose estimation model called FSA Net to improve the model’s performance in complex scenes. Our model utilizes High-Resolution Network (HRNet) as the backbone and feeds the outputs of the last stage’s four branches into the DCB module. The dilated convolution-based (DCB) module employs a parallel structure that incorporates dilated convolutions with different rates to expand the receptive field of each branch. Subsequently, the attention operation-based (AOB) module performs attention operations at both branch and channel levels to enhance high-frequency features and reduce the influence of noise. Finally, predictions are made using the heatmap representation. The model can recognize images with diverse scales and more complex semantic information. Experimental results demonstrate that FSA Net achieves competitive results on the MSCOCO and MPII datasets, validating the effectiveness of our proposed approach.