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