I don't have that much mathematical/theoretical background, so the answer may seem very obvious/trivial to some. I keep seeing "prior" p(x), but what exactly is a prior and what does it represent?

It seems that the authors view the Condensation algorithm as an improvement over Kalman filter contour-tracker, but in section 7, there are times when it seems that the Kalman tracker is used first to obtain training data, which is then used to get the shape and motion models used in the Condensation algorithm. Is there some other way to obtain the shape and motion models?

The prior distribution of x is the assumed distribution of x without any observed evidence. This is in contrast to the posterior distribution of x, p(x|z) in the notation of the paper, which is the distribution of x after observing evidence z.

In the discussion of the dynamical model (sect. 5.2), the authors mention that it would be possible to choose sensible defaults for A x and B, but more satisfactory to estimate them from input data. I would like to know how much more satisfactory, and whether from an implementation standpoint, choosing 'sensible' defaults could mean avoiding the need for input data of 'typical motions', which presumably requires operator knowledge - because it seems that many applications would benefit from as little bootstrapping as possible.

Hi, everyone. I urgently need help about this algorithm.
I'm taking this Condensation Algorithm as my Final Project in visual tracking. I'm having difficulties in understanding the differences between p(x|z) and p(z|x). When and how should I measure each of these? I need p(z|x) to calculate the weights, am I right?
Thanks

65bfc46cb2f3d7e15c5ac0a1cf032473

Hi, Iwan
You've basically have to understand the Bayes Law:
p(x|z) = [p(z|x).p(x)]/p(z) (see http://en.wikipedia.org/wiki/Bayes'_theorem )

You don't measure p(x|z) (posterior density), cause it's hard to obtain. So, you resort to the above expression, since terms in the left hand side of the expression are easier to measure/estimate. You do have to estimate the p(z|x) (conditional density) and there are several methods for doing so.
To fully grasp the theory involved, read the tutorial:
A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking (2001) - PDF: http://citeseer.ist.psu.edu/504843.html

Find the entire source code in C here:
http://www.robots.ox.ac.uk/~misard/condensation.html

Regards

Hi Jorge,
My final project was already completed two years ago.
I finally understand it, although not all of it. But it was working well during the representation.

Thank you very much for your help :) I appreciate it.

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