Human pose estimation (HPE) is a computer vision task that involves determining the position and orientation of the human body, along with the positions of various body parts such as the head, arms, legs, and so on, usually in real-time.
Here's a simplified way to think about it: Imagine you're looking at a photo of a person. You can probably tell what position they're in — maybe they're standing up straight, sitting down, or running. Now imagine trying to teach a computer to understand those same positions, but from any angle and in any lighting. That's essentially what human pose estimation is about.
There are two main types of human pose estimation:
- 2D Pose Estimation: This involves estimating the positions of various body parts in two dimensions — i.e., in an image. This is often done by predicting the positions of key points, or "joints," like the elbows or knees, in the image.
- 3D Pose Estimation: This is a more challenging task that involves estimating the positions of body parts in three dimensions. It not only requires understanding where the body parts are in an image, but also how far they are from the camera.
HPE can be used in many applications, such as in video games for real-time motion capture (think of how characters move in virtual reality games), in healthcare for monitoring physical therapy, or in surveillance for understanding human activities.
In the context of rescue robots, human pose estimation could be used to identify people in need of help. For example, the robot could use human pose estimation to understand if a person is lying down, indicating that they might be injured, or if they're waving their arms, indicating that they're trying to get attention. This kind of information can be extremely valuable when trying to locate and help people in a rescue scenario.