From 5e4c71ae2e8a7a3706522f0acafa79d9cf081513 Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Thu, 1 Mar 2012 03:08:30 -0800
Subject: Data set description

---
 experimental.tex | 51 +++++++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 45 insertions(+), 6 deletions(-)

(limited to 'experimental.tex')

diff --git a/experimental.tex b/experimental.tex
index 2f4d6d4..127cd78 100644
--- a/experimental.tex
+++ b/experimental.tex
@@ -36,14 +36,13 @@ a skeleton was detected by the Microsoft SDK. For each frames the
 following data is available:
 \begin{itemize}
 \item the 3D coordinates of 20 body joints
-\item the z-value: this is the distance from the detected skeleton to
-  the camera
+\item a picture recored by the video camera
 \end{itemize}
-For some of frames, one or several joints were occluded by another
-part of the body. In those cases, the coordinates of these joints are
+For some of frames, one or several joints are occluded by another part
+of the body. In those cases, the coordinates of these joints are
 either absent from the frame or present but tagged as \emph{Inferred}
-by the Microsoft SDK. It means that even though the joint was not
-present on the frame, the skeleton-fitting algorithm was able to guess
+by the Microsoft SDK. It means that even though the joint is not
+present on the frame, the skeleton-fitting algorithm is able to guess
 its location.
 
 Each frame also has a skeleton ID number. If this numbers stays the
@@ -52,6 +51,46 @@ algorithm was able to detect the skeleton in a contiguous way. This
 allows us to define the concept of a \emph{run}: a sequence of frames
 with the same skeleton ID.
 
+Ground truth person recognition is obtained by manually labelling each
+run based on the images captured by the video camera of the
+Kinect. For ease of labelling, only the runs with people walking
+toward the camera are kept. These are the runs where the average
+distance from the skeleton joints to the camera is increasing.
+
+Several reductions are then applied to the data set to extract
+\emph{features} from the raw data:
+\begin{itemize}
+\item from the joints coordinates, the lengths of 15 body parts are
+  computed. These are distances between two contiguous joints in the
+  human body. If one of the two joints of a body part is not present
+  or inferred in a frame, the corresponding body part is reported as
+  absent for that frame.
+\item the number of features is then reduced to 9 by using the
+  vertical symmetry of the human body: if two body parts are symmetric
+  about the vertical axis, we bundle them into one feature by
+  averaging their lengths. If only one of them is present, we take the
+  value of its counterpart. If none of them are present, the feature
+  is reported as missing for this frame. The resulting nine features
+  are: Head-ShoulderCenter, ShoulderCenter-Shoulder, Shoulder-Elbow,
+  Elbow-Wrist, ShoulderCenter-Spine, Spine-HipCenter,
+  HipCenter-HipSide, HipSide-Knee, Knee-Ankle.
+\item finally, all the frames where one of the 9 features is missing
+  are filtered out.
+\end{itemize}
+
+Table \ref{tab:dataset} summarizes some statistics about the resulting
+dataset.
+
+\begin{table}
+\begin{tabular}{cc}
+
+\end{tabular}
+\caption{}
+\label{tab:dataset}
+\end{table}
+
+
+
 
 
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