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diff --git a/experimental.tex b/experimental.tex index 1dedd1d..2f4d6d4 100644 --- a/experimental.tex +++ b/experimental.tex @@ -1,22 +1,26 @@ \section{Experiment design} -A real-life uncontrolled experiment using the kinect was conducted to -test to the algorithm. +We conduct a real-life uncontrolled experiment using the Kinect to test to the +algorithm. First we discuss the signal outputs of the Kinect. Second we +describe the environment in which we collect the data. Finally, we interpret +the data. -\subsection{Kinect} - -Signals: -\begin{itemize} -\item audio -\item video -\item depth map -\end{itemize} - -Skeleton fitting: -\begin{itemize} -\item OpenNI (calibration needed) -\item Microsoft SDK -\end{itemize} +\subsection{Kinect} The Kinect outputs three primary signals in real-time: a +color image stream, a depth image stream, and microphone output. For our +purposes, we focus on the depth image stream. As the Kinect was designed to +interface directly with the Xbox 360~\cite{xbox}, the tools to interact with it +on a PC are limited. Libfreenect~\cite{libfreenect} is a reverse engineered +driver which gives access to the raw depth images from the Kinect. This raw +data could be used to implement the algorithms \eg of +Plagemann~\etal{}~\cite{plagemann:icra10}. Alternatively, +OpenNI~\cite{openni}, a framework sponsored by PrimeSense~\cite{primesense}, +the company behind the technology of the Kinect, offers figure detection and +skeleton fitting algorithms on top of raw access to the data streams. However, +the skeleton fitting algorithm of OpenNI requires each individual to strike a +specific pose for calibration. More recently, the Kinect for Windows +SDK~\cite{kinect-sdk} was released, and its skeleton fitting algorithm operates +in real-time without calibration. Given that the Kinect for Windows SDK is the +state-of-the-art, we use it to perform our data collection. \subsection{Environment} |