  Cascaded regression method is a fast and accurate method on finding 2D pose
of objects in RGB images. It is able to find the accurate pose of objects in an
image by a great number of corrections on the good initial guess of the pose of
objects. This paper explains the algorithm and shows the result of two
experiments carried by the researchers. The presented new method to quickly and
accurately predict 3D positions of body joints from a single depth image, using
no temporal information. We take an object recognition approach, designing an
intermediate body parts representation that maps the difficult pose estimation
problem into a simpler per-pixel classification problem. Our large and highly
varied training dataset allows the classifier to estimate body parts invariant
to pose, body shape, clothing. Finally, we generate confidence-scored 3D
proposals of several body parts by re-projecting the classification result and
finding local modes.
