MS-HRNet: multi-scale high-resolution network for human pose estimation

Human pose estimation has important applications in medical diagnosis (such as early diagnosis of autism in children and assisting with the diagnosis of Parkinson’s disease), human-computer interaction, animation, and other fields. Currently, many human pose estimation algorithms are based on deep learning. However, most research focuses only on increasing the depth and width of the network model. This approach overlooks that merely enlarging the network’s depth and width results in excessive parameterization, without enhancing the model’s effective receptive field or its ability to extract multi-scale features. Hence, this paper constructs a network model, named MS-HRNet (Multi-Scale High-Resolution Network), for human pose estimation. Specifically, we propose a more concise and efficient version of HRNet framework as the backbone network of MS-HRNet. This addresses the challenges of HRNet complex structure and large number of parameters that cause training difficulties, and its inadequacy in handling multi-scale information. Additionally, we designed a multi-scale convolutional kernel parallel module named MSBlock (Multi-Scale Block) as the basic block of MS-HRNet. By introducing coordinate attention modules and ASFF (Adaptive Spatial Feature Fusion ) modules, the model’s ability to extract information is effectively increased, and the issue of feature conflict during the fusion of features with different resolutions is resolved, with only a small increase in the number of model parameters. To evaluate the effectiveness of the proposed model, we conducted comparison experiment and ablation experiments using popular human pose estimation datasets, including COCO2017 and MPII, against multiple existing human pose estimation models.On the COCO 2017 dataset, the number of MS-HRNet parameters are decreased by 41% than the baseline model HRNet, the computational complexity by 59%, and the detection accuracies(mAP) are increased by 2.4 point.