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      trunk/RTCSA_SS/03Design.tex
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trunk/RTCSA_SS/03Design.tex View File

@ -31,20 +31,36 @@ bed, width of sensor square and the FOV of Grideye sensor.
\subsection{Pose determination} \subsection{Pose determination}
We train a SRCNN model by the fused Grideye data and downscaled Lepton 3 image, We train a SRCNN model by the fused Grideye data and downscaled Lepton 3 image,
and use it to upscle all following frames to SR frames. We lebeled some SR frames
and use it to upscale all following frames to SR frames. We labeled some SR frames
to two categories. One is lay on back and the other is lay on side. Since the input to two categories. One is lay on back and the other is lay on side. Since the input
data is very small, we use a neural network consist one 2D convolution layer, one data is very small, we use a neural network consist one 2D convolution layer, one
2D max pooling, one flatten and one densely-connected layer. The possibility of 2D max pooling, one flatten and one densely-connected layer. The possibility of
output has a very large various just after turning over because the model cannot output has a very large various just after turning over because the model cannot
distinguish the residual heat on bed and the person as Figure???? showen. This
distinguish the residual heat on bed and the person as Figure~\ref{fig:pose}(a) shown. This
situation will slowly disappear after one or two minutes. situation will slowly disappear after one or two minutes.
To determination the pose, first we use a median filter with a window size of five To determination the pose, first we use a median filter with a window size of five
to filter out the noise. Than, find the curvex hull line of the upperbound and
lowerbound of the data. Finally calculate the middle line of upperbound and lowerbound.
Figure??? and ??? shows the data and these lines.
to filter out the noise. Than, find the curve hull line of the upper bound and
lower bound of the data. Finally calculate the middle line of upper bound and lower bound.
Figure~\ref{fig:pose}(b) and (c) shows the data and these lines.
We divide every 10 seconds data into a time window. If the middle line of the time window
We divide every data into 10 second time windows. If the middle line of the time window
is at the top one fifth, it is a lay on back. If it is at the bottom one fifth, is at the top one fifth, it is a lay on back. If it is at the bottom one fifth,
it is a lay on side. If the trend of line is going up, it is lay on back. Otherwise, it it is a lay on side. If the trend of line is going up, it is lay on back. Otherwise, it
is lay on side.
is lay on side. To guarantee the confidence of result, we will only trust the pose if
there are three continuously same output.
\begin{figure}[ht]
\centering
\subfloat[Residual heat on bed]{
\includegraphics[width=0.3\columnwidth]{figures/Lepton_residual_heat.bmp}
}
\subfloat[Enhanced Images after Background Calibration]{
\includegraphics[width=0.3\columnwidth]{figures/MinMax.pdf}
}
\subfloat[Enhanced Images after Background Calibration]{
\includegraphics[width=0.3\columnwidth]{figures/Mid.pdf}
}
\caption{Background subtraction.}
\label{fig:pose}
\end{figure}

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