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33 lines
1.3 KiB
33 lines
1.3 KiB
\section{Performance Evaluation}
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\label{sec:eval}
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This section presents the evaluation results for the proposed method, and
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how we collect the dataset.
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For training SRCNN model, we let a person lay on bed and randomly change pose
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or move his arms and legs. Collect the about 600 images from Grideye sensors and
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Lepton 3, and align them at the same timestamps. Figure~\ref{fig:resolution_compare} shows the result of SRCNN model.
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\begin{figure}[tbp]
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\centering
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\subfloat[Grideye Image]{
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\includegraphics[width=0.3\columnwidth]{figures/LR.png}
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}
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\subfloat[SR Image]{
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\includegraphics[width=0.3\columnwidth]{figures/SR.png}
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}
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\subfloat[Downscaled Lepton Image]{
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\includegraphics[width=0.3\columnwidth]{figures/HR.png}
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}
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\caption{Result of SRCNN.}
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\label{fig:resolution_compare}
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\end{figure}
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For training the pose recognition model, we collect 200 images of lay on back and 400 images
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of lay on right or left side. The result shows that the accuracy of single frame detection
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can be improved about 5\% by SRCNN.
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We let a person lay on bed and change his pose every minute. The pose is repeating
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lay on back, lay on left, lay on back and lay on right. Our method will output the current pose
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every 10 seconds and detect the turning over. The accuracy of pose detection is 65\%, and the
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turning over detection has 50\% recall rate and 83\% precision.
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