# High-Resolution Multi-Scale Feature Fusion Network for Running Posture Estimation
## 3 Method
### Dataset
#### 3.4.1 Running Human Dataset  
The Running Human dataset consists of over 1000 original images and 1288 instances of running individuals. All the images in the dataset are sourced from Google Images and the Diamond League official website. We provide a comprehensive set of annotations, including bounding boxes for human detection, position labels for keypoints, and occlusion labels for joints and body parts. Figure 4 depicts some examples from this dataset.  
* Dataset Annotations  
For each image, we initially annotated the bounding box of the human body, then added sequential annotations of human keypoints according to the COCO [5] annotation standard. These keypoints include the eyes, nose, ears, shoulders, elbows, wrists, hips, knees, and ankles, for a total of 17 keypoints. Apart from the nose, all keypoints exhibit clear left-right symmetry. Our images were all captured in running scenarios, encompassing various challenging running postures and covering individuals of different  
Figure 4: Some example images from the Running Human dataset, including single-person example images and multiple-person example images.  
resolutions. We have annotated the main individuals in the collected images while disregarding dense crowds wherein a significant number of human bodies are almost entirely occluded. The Running Human dataset provides a novel and challenging benchmark for running posture estimation.  
* Dataset Split  
We partitioned the dataset into separate training and validation sets. The training set consists of 839 images with 930 instances, while the validation set comprises 277 images with 358 instances. Given that all instances in the dataset correspond to individuals engaged in running activities and that some instances involve occlusions, we recommend initializing the model with weights pre-trained on the COCO [5] dataset. Subsequently, we performed fine-tuning training using the training set of the Running Human dataset to enhance the model's accuracy in the task of running posture estimation.  
* Dataset Augmentation  
Considering that this dataset has a smaller number of images compared to mainstream 2D human pose estimation datasets, it is advisable to employ data-augmentation techniques to expand the training dataset. Data augmentation involves applying a series of random modifications to training images to generate similar yet distinct training samples, thereby increasing the size of the training dataset. Randomly altering training samples can reduce the model's reliance on specific attributes, thereby improving its generalization ability.  
In this dataset, two methods, RandomErasing [22] and GridMask [23], were employed for data augmentation on the training set, resulting in 2197 augmented training images encompassing instances of 2590 runners. The data-augmentation effect is illustrated in Figure 5. The processed images commonly include limb occlusions or extensive occlusions, which facilitate training the network under occlusion conditions, thereby enhancing the network's prediction capability under such conditions.