# High-Resolution Multi-Scale Feature Fusion Network for Running Posture Estimation
###### Abstract  
Running posture estimation is a specialized task in human pose estimation that has received relatively little research attention due to the lack of appropriate datasets. To address this issue, this paper presents the construction of a new benchmark dataset called \"Running Human\", which was specifically designed for running sports. This dataset contains over 1000 images along with comprehensive annotations for 1288 instances of running humans, including bounding boxes and keypoint annotations on the human body. Additionally, a Receptive Field Spatial Pooling (RFSP) module was developed to tackle the challenge of joint occlusion, which is common in running sports images. This module was incorporated into the High-Resolution Network (HRNet) model, resulting in a novel network model named the Running Human Posture Network (RHPNet). By expanding the receptive field and effectively utilizing multi-scale features extracted from the multi-branch network, the RHPNet model significantly enhances the accuracy of running posture estimation. On the Running Human dataset, the proposed method achieved state-of-the-art performance. Furthermore, experiments were conducted on two benchmark datasets. Compared to the state-of-the-art ViTPose-L method, when applied to the COCO dataset, RHPNet demonstrated comparable prediction accuracy while utilizing only one tenth of the parameters and one eighth of the floating-point operations (FLOPs). On the MPII dataset, RHPNet achieves a [EMAIL_ADDRESS] score of 92.0, which is only 0.5 points lower than the state-of-the-art method, PCT. These experimental results provide strong validation for the effectiveness and excellent generalization ability of the proposed method. Keywords:human pose estimation; joint occlusion; multi-branch network; multi-scale features; running posture estimation +
Footnote †: journal: Medical  
depth and limitations in training data. In 2016, researchers proposed the Stacked Hourglass [2] network model, which introduced stacked convolutional layers to capture feature information at different scales through multi-stage convolutional operations and used skip connections to preserve spatial information at different scales. This novel network model greatly improved the accuracy of predictions. In 2018, [PERSON] et al. proposed a simple network architecture called Simple Baseline [3]. This architecture replaced the up-sampling component in Stacked Hourglass with inverse convolutions and included no cross-layer connections between different feature layers in the network. As the name suggests, this work presented a straightforward and effective baseline method for human pose estimation. In 2019, [PERSON] et al. introduced the High-Resolution Network (HRNet) [4] to learn reliable high-resolution representations by connecting the multi-resolution subnetworks in parallel and performing repetitive multi-scale fusion. This multi-branch high-resolution network has achieved remarkable success, surpassing all previous works in three tasks: keypoint detection, pose estimation, and multi-person pose estimation on the Common Objects in Context (COCO) dataset [5]. Inspired by the success of transformer architecture in visual tasks, in 2022, JD Explore Academy partnered with the University of Sydney to employ a straightforward and nonhierarchical vision transformer, named ViTPose [6], for the task of human pose estimation. ViTPose achieved state-of-the-art accuracy on the COCO dataset, underscoring the effectiveness of transformer architecture in the field of human pose estimation. However, the practical application of this approach is limited by the model's parameter size and associated training costs, encompassing training time and hardware requirements. In 2023, Pose as Compositional Tokens (PCT) [7] introduced structured representation into human pose estimation, modeling the dependencies between body joints and automatically learning the sub-structures of human poses. PCT achieved state-of-the-art accuracy on the MPII [8] dataset.