You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
33 lines
1.3 KiB
33 lines
1.3 KiB
\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}[ht]
|
|
\centering
|
|
\subfloat[Grideye Image]{
|
|
\includegraphics[width=0.32\columnwidth]{figures/LR.png}
|
|
}
|
|
\subfloat[SR Image]{
|
|
\includegraphics[width=0.32\columnwidth]{figures/SR.png}
|
|
}
|
|
\subfloat[Downscaled Lepton Image]{
|
|
\includegraphics[width=0.32\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 check if the pose changed. The accuracy of pose detection is 65\%, and the
|
|
turning over detection has 50\% recall rate and 83\% precision.
|