Pose estimation is a computer vision technique used to determine the position and orientation of a person or object by identifying keypoints or joints in an image or video. In the context of human pose estimation, the model detects key body parts such as the head, shoulders, elbows, knees, and ankles, and connects them to represent the skeletal structure of the person. This enables systems to understand human posture, movements, and gestures, which is essential in applications like fitness tracking, animation, augmented reality, and human-computer interaction.
There are two main types of pose estimation: 2D pose estimation, which maps keypoints on a flat image plane, and 3D pose estimation, which reconstructs the pose in three-dimensional space. Modern pose estimation methods often use deep learning models like OpenPose, PoseNet, or HRNet, which can detect multiple people and track complex poses in real-time. Challenges in pose estimation include occlusion, varying body shapes, clothing, and lighting conditions. Nevertheless, advancements in neural network architectures and training datasets have significantly improved the accuracy and robustness of pose estimation systems.