\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 recognization 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.

The accuracy of pose detection is about 67\% and turning over datection is 56\%.