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      trunk/RTCSA_SS/02Background.tex
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      trunk/RTCSA_SS/03Design.tex
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      trunk/RTCSA_SS/05Conclusion.tex
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      trunk/RTCSA_SS/Revisions/RTCSA20_SS_20200608_2.pdf

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trunk/RTCSA_SS/02Background.tex View File

@ -9,8 +9,8 @@ we are using in this work.
C. Dong et al.~\cite{ChaoDong16} proposed a deep learning method for single image
super-resolution (SR). Their method learns how to directly map the low-resolution
image to high-resolution image. They show that the traditional sparse coding
based SR methods can be reformulated into a deep convolutional neural network.
image to high-resolution image. They show that the traditional
SR methods can be reformulated into a deep convolutional neural network.
SRCNN consists three operations.
The first layer of SRCNN model extracts the patches from low-resolution image.
The second layer maps the patches from low-resolution to high-resolution. The
@ -27,10 +27,8 @@ patches.
\subsection{Thermal cameras}
In this work, we use two different resolution thermal cameras to play the role
of low-resolution and high-resolution cameras. For low-resolution camera, we use
Grid-EYE thermal camera. Grid-EYE is a thermal camera that can output
$8 \times 8$ pixels thermal data with $2.5^\circ C$ accuracy and $0.25^\circ C$
resolution at $10$ fps. For the high-resolution one, we use Lepton 3. The
of low-resolution and high-resolution cameras. We use
Grid-EYE thermal camera.and Lepton 3 as our two different inputs. The
specification of them are shown in Table~\ref{table:specificationofdevices}.
@ -53,10 +51,9 @@ specification of them are shown in Table~\ref{table:specificationofdevices}.
\hline
Bits per pixel & 12 & 14\\
\hline
Data Rate & 7.68 kbps & 2.34 mbps\\
\hline
\end{tabular}
\caption{Specification of Grid-EYE~\cite{grideye_datasheet} and Lepton 3~\cite{lepton_datasheet}.}
\caption{Specification of Grid-EYE and Lepton 3.}
%\caption{Specification of Grid-EYE~\cite{grideye_datasheet} and Lepton 3~\cite{lepton_datasheet}.}
\label{table:specificationofdevices}
\end{table}


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trunk/RTCSA_SS/03Design.tex View File

@ -58,8 +58,8 @@ lower bound, and regrad it as the trend of the pose changing. Figure~\ref{fig:tr
shows the filitered data and these lines.
We divide every data into 10 second time windows. If the middle line of the time window
is at the top one fifth, or the trend is going up, it is a lay on back. If it is at the
bottom one fifth, or the trend is going down, it is a lay on side. If there are three
is at the top one fifth, or the trend is going up, it is a lay on back.
Otherwise, it is a lay on side. If there are three
continuously same poses, and different from the last turning over, it will be count as
another turning over.
@ -70,7 +70,7 @@ another turning over.
\caption{Residual heat on bed.}
\label{fig:residual_heat}
\endminipage
\minipage{0.6\columnwidth}
\minipage{0.55\columnwidth}
\includegraphics[width=\linewidth]{figures/MinMax_2.pdf}
\caption{Trend of pose.}
\label{fig:trend}


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trunk/RTCSA_SS/05Conclusion.tex View File

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In this paper, we propose a data fusing and enhancement method to fuse data from multiple low-resolution
thermal sensors and use the SRCNN to enhance resolution, the result shows that
it can improve the accuracy of single frame pose detection by 5\%.
it can slightly improve the accuracy of single frame pose detection.
We also propose a turning over detection method using multiple frames to reduce the noise
and considering the trend of pose change to deal with the residual heat. The result shows
that The accuracy of pose detection is 65\%, and the recall rate and precision of turning over detection
is 50\% and 83\%.
and considering the trend of pose change to deal with the residual heat.

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trunk/RTCSA_SS/Main.tex View File

@ -78,7 +78,7 @@
%\input{MySetting}
\begin{document}
\title{Turning Over Detecting Using Low-Resolution Thermal Sensor}
\title{Turning Over Detecting Using Super-Resolution Thermal Image}
\author{Jyun-Jhe Chou}
%\author{


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trunk/RTCSA_SS/Revisions/RTCSA20_SS_20200608_2.pdf View File


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