From 2ec78535e079c799c03635b834bdfeafe0b4f6e6 Mon Sep 17 00:00:00 2001
From: Jon Whiteaker <jbw@berkeley.edu>
Date: Wed, 20 Mar 2013 23:29:41 -0700
Subject: did a pass on the paper, made some minor changes and did some cutting
 to get it under 8 pages

---
 experimental.tex | 88 ++++++++++++++++++++++++++++++--------------------------
 1 file changed, 48 insertions(+), 40 deletions(-)

(limited to 'experimental.tex')

diff --git a/experimental.tex b/experimental.tex
index 76678ca..a7871eb 100644
--- a/experimental.tex
+++ b/experimental.tex
@@ -34,24 +34,24 @@ results.
 \end{center}
 \end{figure*}
 
-
 The Kinect outputs three primary signals in real-time: a color image stream, a
 depth image stream, and microphone output (\fref{fig:hallway}).  For our
-purposes, we focus on the depth image stream.  As the Kinect was designed to
-interface directly with the Xbox 360, the tools to interact with it on a PC are
-limited.  The OpenKinect project released
-\textsf{libfreenect}~\cite{libfreenect}, a reverse engineered driver which
-gives access to the raw depth images of the Kinect.  This raw data could be
-used to implement skeleton fitting algorithms, \eg those of
-Plagemann~\etal{}~\cite{plagemann:icra10}.  Alternatively,
-OpenNI~\cite{openni}, an open framework led by PrimeSense, the company behind
-the technology of the Kinect, offers figure tracking and skeleton fitting
-algorithms on top of raw access to the data streams.  More recently, the Kinect
-for Windows SDK~\cite{kinect-sdk} was released, also with figure tracking
-and skeleton fitting algorithms.
-%and its skeleton fitting
-%algorithm operates in real-time without calibration.  
-
+purposes, we focus on the depth image stream.  
+%As the Kinect was designed to
+%interface directly with the Xbox 360, the tools to interact with it on a PC are
+%limited.  The OpenKinect project released
+%\textsf{libfreenect}~\cite{libfreenect}, a reverse engineered driver which
+%gives access to the raw depth images of the Kinect.  This raw data could be
+%used to implement skeleton fitting algorithms, \eg those of
+%Plagemann~\etal{}~\cite{plagemann:icra10}.  Alternatively,
+%OpenNI~\cite{openni}, an open framework led by PrimeSense, the company behind
+%the technology of the Kinect, offers figure tracking and skeleton fitting
+%algorithms on top of raw access to the data streams.  More recently, the Kinect
+%for Windows SDK~\cite{kinect-sdk} was released, also with figure tracking
+%and skeleton fitting algorithms.
+%%and its skeleton fitting
+%%algorithm operates in real-time without calibration.  
+%
 We evaluated both OpenNI and the Kinect SDK for skeleton recognition.  The
 skeleton fitting algorithm of OpenNI requires each individual to strike a
 specific pose for calibration, making it more difficult to collect a lot of
@@ -262,20 +262,28 @@ recognition rates mostly above 90\% for group sizes of 3 and 5.
 \subsection{Face recognition}
 
 In the third experiment, we compare the performance of skeleton recognition
-with the performance of face recognition as given by \textsf{face.com}.  At the
-time of writing, this is the best performing face recognition algorithm on the
-LFW dataset\footnote{\url{http://vis-www.cs.umass.edu/lfw/results.html}}.
-
-We use the REST API of \textsf{face.com} to do face recognition on our dataset.
-Due to the restrictions of the API, for this experiment we set $n_p = 5$ and
-train on one half of the data and test on the remaining half. For comparison,
-the MoG algorithm is run with the same training-testing partitioning of the
-dataset. In this setting, SHT is not relevant for the comparison, because
-\textsf{face.com} does not give the possibility to mark a sequence of frames as
-belonging to the same run. This additional information would be used by the SHT
-algorithm and would thus bias the experiment in favor of skeleton recognition.
-The results are shown in \fref{fig:face}.  Skeleton recognition performs
-within 10\% of face recognition at most thresholds.
+with the performance of face recognition.  For this experiment we set $n_p = 5$
+and train on one half of the data and test on the remaining half. For
+comparison, the MoG algorithm is run with the same training-testing
+partitioning of the dataset.  The results are shown in \fref{fig:face}.
+Skeleton recognition performs within 10\% of face recognition at most
+thresholds.
+
+%In the third experiment, we compare the performance of skeleton recognition
+%with the performance of face recognition as given by \textsf{face.com}.  At the
+%time of writing, this is the best performing face recognition algorithm on the
+%LFW dataset\footnote{\url{http://vis-www.cs.umass.edu/lfw/results.html}}.
+%
+%We use the REST API of \textsf{face.com} to do face recognition on our dataset.
+%Due to the restrictions of the API, for this experiment we set $n_p = 5$ and
+%train on one half of the data and test on the remaining half. For comparison,
+%the MoG algorithm is run with the same training-testing partitioning of the
+%dataset. In this setting, SHT is not relevant for the comparison, because
+%\textsf{face.com} does not give the possibility to mark a sequence of frames as
+%belonging to the same run. This additional information would be used by the SHT
+%algorithm and would thus bias the experiment in favor of skeleton recognition.
+%The results are shown in \fref{fig:face}.  Skeleton recognition performs
+%within 10\% of face recognition at most thresholds.
 %outperforms
 %skeleton recognition, but by less than 10\% at most thresholds. 
 %These results are promising, given that \textsf{face.com} is the
@@ -288,8 +296,8 @@ within 10\% of face recognition at most thresholds.
 \subsection{Walking away}
 
 In the next experiment, we include the runs in which people are walking away
-from the Kinect that we could positively identify.  The performance of face
-recognition outperforms skeleton recognition in the previous setting.  However,
+from the Kinect that we could positively identify.  While, face
+recognition outperforms skeleton recognition in the previous setting,
 there are many cases where only skeleton recognition is possible. For example,
 when people are walking away from the Kinect. Coming back to the raw data
 collected during the experiment design, we manually label the runs of people
@@ -310,14 +318,6 @@ they are walking towards the camera.
 %  \label{fig:back}
 %\end{figure}
 
-\begin{figure}[t]
-  \centering
-  \includegraphics[width=0.49\textwidth]{graphics/back.pdf}
-\vspace{-1.5\baselineskip}
-\caption{Results with people walking away from and toward the camera}
-\label{fig:back}
-\end{figure}
-
 \fref{fig:back} compares the results obtained in \xref{sec:experiment:offline}
 with people walking toward the camera, with the results of the
 same experiment on the dataset of runs of people walking away from the camera.
@@ -330,6 +330,14 @@ from the camera with similar performance.  Note that while we could not obtain
 enough labeled data for a full comparison when it is dark, manual experiments
 show similar performance when there is no visible light.
 
+\begin{figure}[t!]
+  \centering
+  \includegraphics[width=0.49\textwidth]{graphics/back.pdf}
+\vspace{-1.5\baselineskip}
+\caption{Results with people walking away from and toward the camera}
+\label{fig:back}
+\end{figure}
+
 \subsection{Reducing the noise} 
 
 For the final experiment, we explore the potential of skeleton recognition with
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