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