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| author | Jon Whiteaker <jbw@berkeley.edu> | 2012-08-13 15:46:25 -0700 |
|---|---|---|
| committer | Jon Whiteaker <jbw@berkeley.edu> | 2012-08-13 15:46:25 -0700 |
| commit | cef7389306738767b76ba32437150fd20880c350 (patch) | |
| tree | 3b6086b3bdbd4e33480e731addd9f0e1f6afafb4 | |
| parent | ff1e46e5bfa8ba1aad2f8f11aa3b701ac76a3847 (diff) | |
| download | kinect-cef7389306738767b76ba32437150fd20880c350.tar.gz | |
made reducing the noise section more clear
| -rw-r--r-- | experimental.tex | 17 |
1 files changed, 10 insertions, 7 deletions
diff --git a/experimental.tex b/experimental.tex index 07fd74b..909e4cb 100644 --- a/experimental.tex +++ b/experimental.tex @@ -361,19 +361,22 @@ from the camera with similar performance. \subsection{Reducing the noise} -For the final experiment, we study what happens when the noise is reduced on -the Kinect. +For the final experiment, we explore the potential of skeleton recognition with +a higher resolution depth camera, as has been speculated for the Kinect +2~\footnote{\url{http://www.eurogamer.net/articles/2011-11-25-kinect-2-so-accurate-it-can-lip-read}}. +Since a higher resolution camera is not readily available, we simulate a higher +resolution by artificially reducing the noise from our Kinect dataset. %Predicting potential improvements of the prediction rate of our %algorithm is straightforward. The algorithm relies on 9 features only. %\xref{sec:uniqueness} shows that 6 of these features alone are sufficient to %perfectly distinguish two different skeletons at a low noise level. Therefore, %the only source of classification error in our algorithm is the dispersion of %the observed limbs' lengths away from the exact measurements. -To simulate a reduction of the noise level, the dataset is modified as -follows: we compute the average profile of each person, and for each frame we -divide the empirical variance from the average by 2. Formally, using -the same notations as in Section~\ref{sec:mixture of Gaussians}, each -observation $\bx_i$ is replaced by $\bx_i'$ defined by: +To simulate a reduction of the noise level, the dataset is modified as follows: +we measure the average skeletal profile of each person, and for each frame +we divide the empirical variance from the average by 2. Formally, using the +same notations as in Section~\ref{sec:mixture of Gaussians}, each observation +$\bx_i$ is replaced by $\bx_i'$ defined by: \begin{equation} \bx_i' = \bar{\bx}_{y_i} + \frac{\bx_i-\bar{\bx}_{y_i}}{2} \end{equation} |
