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Diffstat (limited to 'intro.tex')
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1 files changed, 14 insertions, 16 deletions
@@ -11,7 +11,7 @@ recognition. In recent years, advances in range cameras have given us access to increasingly accurate real-time depth imaging. Furthermore, the low-cost and widely available Kinect~\cite{kinect} has brought range imaging to the masses. In -parallel, the automatic detection of body parts from depth images has led to +parallel, automatic detection of body parts from depth images has led to real-time skeleton fitting. %In this paper we show that skeleton fitting is accurate and unique enough in @@ -19,10 +19,10 @@ real-time skeleton fitting. We make the following contributions. First, we show that ground truth skeleton measurements can uniquely identify a person. Second, we propose two models for skeleton recognition. Finally, we evaluate our hypothesis using real-world -data collected with the Kinect. Our results show that skeleton recognition can +data collected with a Kinect. Our results show that skeleton recognition can identify three people with 95\% accuracy, and five people with 85\% accuracy. -Furthermore, skeleton recognition can be performed in more situations than face -recognition, such as when a person is not facing the camera. +Furthermore, skeleton recognition can be performed in situations where face +recognition cannot, such as when a person is not facing the camera. %As the resolution and accuracy of range cameras improve, so will the accuracy %and precision of skeleton fitting algorithms. @@ -33,16 +33,14 @@ recognition, such as when a person is not facing the camera. The paper is organized as follows. First we discuss prior methods of person recognition, in addition to the advances in the technologies -pertaining to skeleton fitting (Section~\ref{sec:related}). Next we -use a dataset of actual skeletal measurements to show that -skeletons are a unique enough descriptor for person recognition. -(Section~\ref{sec:uniqueness}). We then discuss an error model and the -resulting algorithm to do person recognition -(Section~\ref{sec:algorithms}). 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}). +pertaining to skeleton fitting (Section~\ref{sec:related}). Next we use a +dataset of actual skeletal measurements to show that skeletons are a unique +enough descriptor for person recognition. (Section~\ref{sec:uniqueness}). We +then discuss an error model and the resulting algorithm to do person +recognition (Section~\ref{sec:algorithms}). Finally, we collect skeleton data +with a Kinect in an uncontrolled setting. We apply preprocessing and +classification algorithms to this dataset and evaluate the performance of +skeleton recognition with varying group size (Section~\ref{sec:experiment}). +We conclude by discussing challenges working with the Kinect and future work +(Section~\ref{sec:conclusion}). |