Attentional 2D-Human Pose Estimation

Attentional 2D-Human Pose Estimation refers to a technique used in computer vision and deep learning to estimate the pose of a human body in an image or a video. The technique involves the use of a neural network that has an attention mechanism. This mechanism enables the network to focus on specific regions of the image that are relevant to the pose estimation task.

In the case of 2D-Human Pose Estimation, the neural network takes an input image and predicts the 2D coordinates of the joints of the human body. The attention mechanism allows the network to focus on the body parts that are most important for the pose estimation task, such as the head, shoulders, elbows, wrists, hips, knees, and ankles.

The attentional 2D-Human Pose Estimation technique has shown promising results in various applications, including activity recognition, emotion recognition, and human-computer interaction. It has the potential to be used in fields such as sports analysis, healthcare, and robotics.