Human Pose Estimation (HPE) is a task in computer vision that involves detecting and estimating the positions of various body parts in images or videos of people.It can be used to understand human posture, movement, and behavior, as well as to implement applications such as human-computer interaction, video surveillance, and motion analysis. Human pose estimation can be done using a variety of techniques, and a common approach is to use machine learning algorithms to discover the characteristics of various body parts and their relationships to each other. This may require developing a model using a set of labeled pictures or videos where the positions of body parts have been manually recorded. The trained model can then be used to predict the positions of body parts in new, previously unseen photos or videos.

Another approach to human pose estimation involves using geometric models to represent the body and its parts and fitting these models to images or videos. This may involve estimating the locations of key points (such as joints) and using them to infer the locations of other body parts.

Human pose estimation is a challenging task, especially when the person is occluded or in poor lighting conditions. Accurately estimating the pose of people with large variations in body size or shape can also be difficult. In order to improve the accuracy and robustness of human pose estimation algorithms, it is often necessary to use large, diverse datasets and advanced machine learning techniques.