Hi javi:
Microsoft use the machine learning approach to detect the human
pose,the Bayesian probabilistic decision is a very common and robust
way to do the computer vision job.
I don't know how OpenNI did it, but I'm trying to use facial
detection to get
the position of human's head, and then use some kinds of "Hough
transform" (
http://en.wikipedia.org/wiki/Hough_transform) to detect
the lines around the human's head, because the limbs are straight
lines in the data from depth camera ,and then I assemble the lines
together to make the human pose, in most of the case it is working,
I made a simple test yesterday:
http://www.youtube.com/watch?v=WKwvyRfuxBA
but I have 3 major problems:
1 the result is not stable, shaking all the time.
2 if the angel of limb perpendicular to the camera's projection
plane,
it will get error,because there will be no line on the plane anymore,
just a projection point.
3 if the user turn their head more than 60 degree to the camera, then
the facial recognition will fail, the whole process will get error
I'm doubting my approach may fundamentally wrong ,I'm looking for
anybody who have any fresh ideas about this, or could indicate me some
paper/thesis
> could not retrieve any relevant publications from them... (
http://www.wired.co.uk/magazine/archive/2010/11/features/the-game-changer?pa...)
>
> Thanks in advance!
>
> Javi