Preparing Data for the Human Pose Estimation Task

A method for collecting and preparing data for use in training neural networks in the field of Human Pose Estimation has been demonstrated. This area is highly relevant in computer vision, as it enables applications such as automatic background replacement, motion capture, augmented reality, and virtual reality. The publication presents the results of research into the challenges encountered in this field during the collection of real and synthetic data. It highlights the limitations of existing datasets, which often fail to cover rare or complex scenarios, such as extreme poses, rapid movements, or actions in unconventional conditions. To address these gaps, the study explores the use of synthetic data generated through advanced techniques, including procedural modeling, physically accurate rendering, and animation in 3D environments like Blender. The research investigates the impact of the amount of synthetic data on training quality and identifies the optimal ratio of synthetic to real data to achieve the best training results. For instance, it was found that a 1:2 ratio of synthetic to real data provides the most balanced performance, although this may vary depending on the specific dataset and neural network architecture. The work provides graphical and textual representations of human body joints, proposing a topology of 33 keypoints that includes additional points on hands and feet to better capture body orientation. A segmented model of the human body is also presented, which is crucial for tasks like image segmentation and pose estimation. Approaches to increasing the volume of such data and their processing are proposed with the aim of improving the accuracy of trained models in various scenarios. These include data augmentation techniques, such as rotation, scaling, brightness adjustment, and noise addition, as well as tools for efficient annotation. Additionally, existing datasets and tools for annotating them are provided, offering a comprehensive resource for researchers in the field. The study underscores the importance of meticulous data preparation for achieving high training accuracy, particularly when analyzing complex objects like the human body.