In computer vision, articulated body pose estimation is the task of algorithmically determining the pose of a body composed of connected parts (joints and rigid parts) from image or video data.
This challenging problem, central to enabling robots and other systems to understand human actions and interactions, has been a long-standing research area due to the complexity of modeling the relationship between visual observations and pose, as well as the wide range of applications.
[1][2] Enabling robots to perceive humans in their environment is crucial for effective interaction.
For example, interpreting pointing gestures requires the ability to recognize and understand human body pose.
This makes pose estimation a significant and challenging problem in computer vision, driving extensive research and development of numerous algorithms over the past two decades.
Many successful approaches rely on training complex models with large datasets.
Articulated pose estimation is particularly difficult due to the high dimensionality of human movement.
While not all joint movements are readily apparent, even a simplified representation of the body with 10 major parts and 20 degrees of freedom presents considerable challenges.
Algorithms must account for substantial appearance variations caused by clothing, body shape, size, and hairstyles.
Furthermore, most algorithms operate on monocular (2D) images, which lack inherent 3D information, exacerbating the ambiguity.
Recent research explores the use of RGB-D cameras, which capture both color and depth information, to address the limitations of monocular approaches.
The representations include the following: The basic idea of part based model can be attributed to the human skeleton.
To formulate the model so that it can be represented in mathematical terms, the parts are connected to each other using springs.
is given by The above equation simply represents the spring model used to describe body pose.
One such example is the flexible mixture model which reduces the database of hundreds or thousands of deformed parts by exploiting the notion of local rigidity.
Since about 2016, deep learning has emerged as the dominant method for performing accurate articulated body pose estimation.
The first deep learning models that emerged focused on extracting the 2D positions of human joints in an image.
[10][11] When there are multiple people per image, two main techniques have emerged for grouping joints within each person.
In the first, "bottom-up" approach, the neural network is trained to also generate "part affinity fields" which indicate the location of limbs.
Such approaches often project image features into a cube and then use a 3D convolutional neural network to predict a 3D heatmap for each joint.
Most of the work is based on estimating the appropriate pose of the skinned multi-person linear (SMPL) model[21] within an image.
Personal care robots may be deployed in future assisted living homes.
Recent advances in pose estimation and motion capture have enabled markerless applications, sometimes in real time.
As such, an intelligent system tracking driver pose may be useful for emergency alerts [dubious – discuss].
[citation needed] Commercially, pose estimation has been used in the context of video games, popularized with the Microsoft Kinect sensor (a depth camera).
[26] Pose estimation has been used to detect postural issues such as scoliosis by analyzing abnormalities in a patient's posture,[27] physical therapy, and the study of the cognitive brain development of young children by monitoring motor functionality.
[28] Other applications include video surveillance, animal tracking and behavior understanding, sign language detection, advanced human–computer interaction, and markerless motion capturing.
A commercially successful but specialized computer vision-based articulated body pose estimation technique is optical motion capture.
This approach involves placing markers on the individual at strategic locations to capture the 6 degrees-of-freedom of each body part.