## 1 Introduction
Currently, there is a scarcity of dedicated datasets in the existing literature that has been specifically designed for the purpose of identifying the key joint positions of the human body during normal gait movements [4]. Therefore, the primary objective of this research was to address this gap by developing a comprehensive dataset that includes data from various subjects performing their normal gait. Furthermore, the proposed dataset was utilized to train a deep learning model, enabling the creation of a 2D estimator that can infer the 2D key joint positions of a person from the 2D images captured using a simple camera system. To achieve this objective, we utilized the VICON motion capture system [10] to collect motion data from a diverse range of subjects in a controlled motion capture environment while they performed normal gait. The objective of the dataset was to encompass a wide range of gait motions and to capture the intricate complexities of human gait in real-world scenarios. The NVIDIA TAO's BPNET (Body Pose Network), a deep learning framework specifically designed for key joint position identification of a person, was utilized as a part of this research [11]. The selection of the NVIDIA TAO was driven by its extensive adoption and recognition in the computer vision community. This deep learning toolkit is well-regarded for its user-friendly interface and capability to facilitate rapid prototyping and deployment of deep learning models on NVIDIA GPUs [12]. The objective of training the model with our dataset was to validate the accuracy and robustness of the proposed dataset in identifying the key joint positions during different gait movements. The general workflow and entities of the proposed dataset have been demonstrated in Figure 1.  
To assess the effectiveness of our approach, we conducted a comparative analysis between the performance of the BPNET trained on our dataset and a model trained on the widely used COCO 2017 (Common Objects in Context) [13] dataset. The COCO 2017 dataset has been extensively utilized in computer vision research for a variety of tasks, including object detection, segmentation, and human key feature identification tasks [5; 6; 14]. By contrasting the performance of our model with that of the COCO-based model, we evaluated the advantages and potential enhancements achieved through our dataset and training methodology. This paper's contributions are as follows. Existing datasets for human pose estimation frequently prioritize quantity over quality. While large-scale datasets, such as COCO 2017, have been instrumental in advancing pose estimation models, their focus on vast image collections can introduce limitations. These limitations include inconsistencies in annotation detail and lower image resolution, which can hinder the accuracy of key point prediction for specific tasks such as gait analysis. Furthermore, existing datasets may not fully encompass the intricacies of human movement as effectively as dedicated motion capture systems. This study addresses these limitations by introducing a novel high-resolution dataset specifically designed for accurate key point identification during gait analysis. The dataset employs the precision of the VICON motion capture system to capture detailed three-dimensional ground truth data in conjunction with high-resolution images from multiple perspectives. This combination offers a more comprehensive and accurate representation of human movement than is possible with existing datasets. Figure 1: General workflow (the proposed dataset consists of the 2D images captured in different views and 2D key point position acquired from the transformation of VICON recordings, along with the segmentations and bounding box discussed in Section 3.1).