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| -rw-r--r-- | experimental.tex | 11 | ||||
| -rw-r--r-- | intro.tex | 26 |
2 files changed, 22 insertions, 15 deletions
diff --git a/experimental.tex b/experimental.tex index cfdaf78..982fcf7 100644 --- a/experimental.tex +++ b/experimental.tex @@ -1,4 +1,5 @@ \section{Real-World Evaluation} +\label{sec:experiment} We conduct a real-life uncontrolled experiment using the Kinect to test to the algorithm. First we present the manner and environment in which we perform @@ -35,7 +36,8 @@ the RGB camera. \begin{center} \includegraphics[width=0.99\textwidth]{graphics/hallway.png} \end{center} - \caption{} + \caption{Experiment setting. Color image, depth image, and fitted + skeleton as captured by the Kinect in a single frame} \label{fig:hallway} \end{figure} @@ -77,6 +79,10 @@ with the same skeleton ID. \begin{table} \begin{center} +\caption{Data set statistics. The right part of the table shows the +average numbers for different intervals of $k$, the rank of a person +in the ordering given by the number of frames} + \begin{tabular}{|l|r||r|r|r|} \hline Number of people & 25 & $k\leq 5$ & $5\leq k\leq 20$ & $k\geq 20$\\ @@ -87,9 +93,6 @@ Number of runs & 244 & 18 & 8 & 4\\ \hline \end{tabular} \end{center} -\caption{Data set statistics. The right part of the table shows the -average numbers for different intervals of $k$, the rank of a person -in the ordering given by the number of frames.} \label{tab:dataset} \end{table} @@ -31,15 +31,19 @@ Xbox 360~\cite{} does use the height inferred from the Kinect as part of its user identification algorithm, albeit in addition to other attributes including face recognition. -The paper is organized as follows. First we discuss prior methods of person -identification, in addition to the advances in the technologies pertaining to -skeleton mapping (Section~\ref{sec:related}). Next we use a dataset of actual -skeletal measurements to show that identification by skeleton is feasible, even -when we simulate the error of measuring skeletons with a Kinect -(Section~\ref{sec:}). Finally, we Lastly, we collect skeleton data with a -Kinect in an uncontrolled setting and we apply preprocessing and classification -algorithms to this dataset (Section~\ref{sec:}). We evaluate the performance -of skeleton recognition with varying group size and compare it to face -recognition. We conclude by discussing challenges working with the Kinect and -future work (Section~\ref{sec:conclusion}). +The paper is organized as follows. First we discuss prior methods of +person identification, in addition to the advances in the technologies +pertaining to skeleton mapping (Section~\ref{sec:related}). Next we +use a dataset of actual skeletal measurements to show that +identification by skeleton is feasible, even when we simulate the +error of measuring skeletons with a Kinect +(Section~\ref{sec:uniqueness}). We then discuss an error model and the +resulting algorithm to do person identification +(Section~\ref{sec:algorithm}). Finally, we collect skeleton data with +a Kinect in an uncontrolled setting and we apply preprocessing and +classification algorithms to this dataset +(Section~\ref{sec:experiment}). We evaluate the performance of +skeleton recognition with varying group size and compare it to face +recognition. We conclude by discussing challenges working with the +Kinect and future work (Section~\ref{sec:conclusion}). |