Monocular 3D human pose estimation is a process that involves predicting the 3D locations of various body parts using only a single RGB camera. This task has applications in various fields, such as sports analysis, human-computer interaction, and health monitoring.
What is Monocular 3D Human Pose Estimation?
Human pose estimation is the process of detecting and locating the body parts of humans in images or videos. This process is significant in various fields, such as sports analysis, computer vision, and autonomous robotics. Traditional methods used multi-camera setups or depth cameras to achieve accurate 3D human pose estimation. But these options are either expensive or not sufficiently convenient for practical purposes.
Monocular 3D human pose estimation aims to solve this problem by predicting the 3D coordinates of human body parts from a single RGB camera. This process involves using machine learning algorithms and computer vision techniques to analyze the 2D image of the human body and infer its 3D pose. Monocular 3D human pose estimation is still an emerging technology and is still in its infancy as it is very challenging.
How Does Monocular 3D Human Pose Estimation Work?
Monocular 3D human pose estimation works by predicting the location of each body joint in 3D from a single 2D image captured by an RGB camera. The process involves several steps:
1. Human Detection
The first step in the process is detecting the human from a visual stream. This detection is done through image segmentation, which involves separating the human from the background. Convolutional Neural Network (CNN) is frequently used for this process as it can distinguish regions of relevance from irrelevant regions.
2. Joint Localization in 2D Space
The second step is localizing the body's keypoints in 2D space from the image. Keypoints represent critical locations in the human body, such as the head, elbows, knees, etc. This step is essential as it helps in recognizing the pose of a human subject in the image. CNN and other deep learning techniques can perform this task accurately.
3. Predicting 3D Coordinates
After localizing the body joints in 2D, the algorithm uses pose estimation techniques, including Convolutional Pose Machines (CPMs) or Regression trees, to predict the 3D coordinates of the body joints. These techniques use a set of 2D coordinates (x, y) as inputs and generate predicted 3D coordinates (x, y, z) for all joints
Applications of Monocular 3D Human Pose Estimation
Monocular 3D human pose estimation has numerous applications across various fields. Here are some of the most notable:
Sports Analysis
Monocular 3D human pose estimation helps understand the body movements of athletes in real-time. This technology enables coaches and players to analyze the performance of the game and provide feedback to improve their strategy.
Health Monitoring
Monocular 3D human pose estimation can help monitor the posture of individuals and alert them if they are sitting or standing in ways that are detrimental to their health. This technology provides a non-invasive way of detecting and correcting body postures and movements that may lead to chronic pain and musculoskeletal disorders.
Human-Computer Interaction
Monocular 3D human pose estimation enables people to interact with computers and other devices in more intuitive ways. It enables devices like Virtual and augmented reality systems to detect and track human motion and adjust their output accordingly. Users can also control and operate devices through body movements, which provide a more natural and practical interface.
Monocular 3D human pose estimation is still an emerging technology that has the potential to revolutionize the way humans interact with the digital world. This process has a wide range of applications in fields like health monitoring, sports analysis, and Human-Computer Interaction. While still imperfect, with lots of studies ongoing, researchers are hopeful that it is a probability that they will be able to make this technology available for many people to use easily at home or work.
