Articulated body pose estimation

From Wikipedia, the free encyclopedia
Jump to: navigation, search

Articulated body pose estimation, in computer vision, is the study of algorithms and systems that recover the pose of an articulated body, which consists of joints and rigid parts using image-based observations. It is one of longest-lasting problems in computer vision because of the complexity of the models that relate observation with pose, and because of the variety of situations in which it would be useful.[1][2]


Perception of human beings in neighboring environment is an important capability Robots must possess. If a person uses gestures to point to a particular object, then the interacting machine should be able to understand the situation in real world context. Hence pose estimation is an important and challenging problem in computer vision and many algorithms have been deployed in solving this problem over last two decades. Many state of the art models have been developed to solve this problem, models are first trained using large data sets and then used in real world application.

Pose estimation is a difficult problem and still a subject of research because the human body has 244 degrees of freedom with 230 joints. Not all movements between joints are evident, but the human body is composed of ten large parts with 20 degrees of freedom. Moreover, appearance differences due to clothing, body shape, scale (e.g. tall, fat, short body), and hairstyles cause large variability, and an algorithm must account for that. Partial occlusions due to self-articulation (hand covering the face or the frontal portion of the body) or occlusions due to external objects may cause ambiguities in the results obtained. Also, most of the algorithms estimate pose from two-dimensional images, the ones captured from the normal camera. These images lack the three-dimensional information of an actual body pose. There is recent work in this area wherein images from RGBD cameras provide information about color and depth.[3]

There is a need to develop accuratelytether-less, vision-based articulated body pose estimation systems to recover the pose of bodies such as the human body, a hand, or non-human creatures. Such a system have several foreseeable applications, including

One of the major difficulties in recovering pose from images is the high number of degrees-of-freedom (DOF) in the body's movement that has to be recovered. Any rigid object requires six DOF to fully describe its pose, and each additional rigid object connected to it adds at least one DOF. A human body contains no less than 10 large body parts, equating to more than 20 DOF. This difficulty is compounded by the problem of self-occlusion, where body parts occlude each other depending on the configuration of the parts. Other challenges involve dealing with varying lighting, which affect appearance; varying subject attire or body type; required camera configuration; and required computation time.

The typical articulated body pose estimation system involves a model-based approach, in which the pose estimation is achieved by maximizing/minimizing a similarity/dissimilarity between an observation (input) and a template model. Different kinds of sensors have been explored for use in making the observation, including

These sensors produce intermediate representations that is directly used by the model; the representations include

  • Image appearance,
  • Voxel (volume element) reconstruction,
  • 3D point clouds, and sum of Gaussian kernels[5]
  • 3D surface meshes.

Part Models[edit]

The basic idea of part based model can be attributed to human skeleton. Any object having property of articulation can be disintegrated into smaller parts wherein each part can take different orientation resulting in different articulations of the same object. Hence different scales and orientation of the main object can be articulated to scales and orientation of the corresponding parts. To formulate the model so that it can be represented in mathematical terms, the parts are connected to each other using springs. Hence the model is also known as Spring model. The degree of closeness between each part is accounted by the compression and expansion of the springs. There is geometric constrain on the orientation of springs. For example, limbs of legs cannot move 360 degrees. Hence parts cannot have that extreme orientation. This reduces the possible permutations.[6]

The spring model forms a graph G(V,E) where V (nodes) corresponds to the parts and E (edges) represent springs connecting two neighboring parts. Each location in image can be reached by the and coordinates of the pixel location. Let be point at location. Then the cost associated in joining the spring between and the point can be given by . Hence the total cost associated in placing components at locations is given by

The above equation simply represents the spring model used to describe body pose. To estimate pose from images cost or energy function must be minimized. This energy function consists of two terms, one related to how each component matches image data and second deals with how much do the oriented(deformed) parts match thus accounting for articulation along with object detection.[7]

The part models also known as pictorial structures is one the basic models on which other efficient models are built by slight modification. One such example is flexible mixture model which reduces the database of hundreds or thousands of deformed parts by exploiting the notion of local rigidity.[8]

Articulated Model with Quaternion [9][edit]

The kinematic skeleton is constructed by a tree-structured chain, as illustrated in the Figure. Each rigid body segment has its local coordinate system that can be transformed to the world coordinate system via a 4×4 transformation matrix ,

where denotes the local transformation from body segment to its parent . Each joint in the body has 3 degrees of freedom (DoF) rotation. Given a transformation matrix , the joint position at the T-pose can be transferred to its corresponding position in the world coordination. In many works, the 3D joint rotation is expressed as a normalized quaternion due to its continuity that can facilitate gradient-based optimization in the parameter estimation.


Assisted Living[edit]

For old age homes, nursing homes soon personal care robots will be deployed in near future. Human detection, pose estimation with high accuracy is a requirement. Also the models used should not be memory intensive.

Movies, 3D TV and animation[edit]

Avatar animation is one such example where human pose helps in developing animation.

Intelligent driver assisting system[edit]

Car accidents account for 2.2% of death globally each year. Hence an intelligent system tracking drivers pose constantly will be of great use as emergency alerts can Successful pedestrian detection algorithms have proved to be useful for autonomous cars. Along with real time detection if nature or behavior of pedestrians can be predicted beforehand, the decision capability of driver assisting system would increase.

Other Applications[edit]

Other applications include Physical therapy, study of cognitive brain development of young children, video surveillance, animal tracking and behavior understanding to preserve endangered species, sign language detection,advanced human computer interaction, marker less motion capturing.

Sensors Used[edit]

Marker based systems, Laser range scanners, Structured Light, Time of flight sensors, MicrosoftKinect sensor, Multi camera Systems.

Related technology[edit]

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.

Active Research Groups[edit]

A number of groups are actively pursuing this topic, including groups in Brown University; Carnegie Mellon University; MPI Saarbruecken; Stanford University; the University of California, San Diego; the University of Toronto; the Ecole Centrale de Paris; ETH Zurich; National University of Sciences and Technology (NUST)[10] and UC Irvine;

Implementation Links[edit]


  1. ^ Survey of Computer Vision-Based Human Motion Capture (2001)
  2. ^ Survey of Advances in Computer Vision-based Human Motion Capture (2006)
  3. ^ Droeschel, David, and Sven Behnke. "3D body pose estimation using an adaptive person model for articulated ICP." Intelligent Robotics and Applications. Springer Berlin Heidelberg, 2011. 157167.
  4. ^ Han, J.; Gaszczak, A.; Maciol, R.; Barnes, S.E.; Breckon, T.P. (September 2013). "Human Pose Classification within the Context of Near-IR Imagery Tracking". Proc. SPIE Optics and Photonics for Counterterrorism, Crime Fighting and Defence (PDF). 8901. SPIE. pp. 1–10. doi:10.1117/12.2028375. Retrieved 5 November 2013. 
  5. ^ M. Ding and G. Fan, "Generalized Sum of Gaussians for Real-Time Human Pose Tracking from a Single Depth Sensor" 2015 IEEE Winter Conference on Applications of Computer Vision (WACV), Jan 2015
  6. ^ Fischler, Martin A., and Robert A. Elschlager. "The representation and matching of pictorial structures." IEEE Transactions on computers 1 (1973): 6792.
  7. ^ Felzenszwalb, Pedro F., and Daniel P. Huttenlocher. "Pictorial structures for object recognition." International Journal of Computer Vision 61.1 (2005): 5579.
  8. ^ Yang, Yi, and Deva Ramanan. "Articulated pose estimation with flexible mixturesofparts." Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011.
  9. ^ M. Ding and G. Fan, "Articulated and Generalized Gaussian Kernel Correlation for Human Pose Estimation" IEEE Transactions on Image Processing, Vol. 25, No. 2, Feb 2016
  10. ^

External links[edit]