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Thomas Forgione 2019-10-19 16:51:12 +02:00
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src/system-bookmarks/user-study.tex
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@ -4,7 +4,7 @@
\subsection{Preliminary user study} \subsection{Preliminary user study}
Before conducting the user study on mobile devices, we designed a preliminary user study for desktop devices. Before conducting the user study on mobile devices, we designed a preliminary user study for desktop devices.
This experiment was conducted on twelve users, using the 3D model described in the previous chapter (i.e. the Marina Bay district in Singapore). This experiment was conducted on twelve users, using the 3D model described in the previous chapter (i.e.\ the Marina Bay district in Singapore).
Bookmarks were sampled from the set of locations of user-uploaded panoramic pictures available on Google Maps, and the task consisted in matching real-world pictures to their virtual location on the 3D model: users were presented with an image coming from Google Street View and they were asked to find the exact same location in the 3D model. Bookmarks were sampled from the set of locations of user-uploaded panoramic pictures available on Google Maps, and the task consisted in matching real-world pictures to their virtual location on the 3D model: users were presented with an image coming from Google Street View and they were asked to find the exact same location in the 3D model.
Due to the great difficulty of the task was, as well as the relative familiarity of the users with 3D navigation, the user behaviour was biased towards navigating slowly in the scene. The users almost never clicked the bookmarks, much less as they did during the experiment we ran in Chapter~\ref{bi}. Due to the great difficulty of the task was, as well as the relative familiarity of the users with 3D navigation, the user behaviour was biased towards navigating slowly in the scene. The users almost never clicked the bookmarks, much less as they did during the experiment we ran in Chapter~\ref{bi}.
@ -130,8 +130,8 @@ We only proposed this user study to relatively young people to ensure they are u
yshift=-0.5cm, yshift=-0.5cm,
] ]
\addplot[smooth, color=DarkGreen] table [y=y, x=x]{assets/system-bookmarks/final-results/second-experiment-2.dat}; \addplot[smooth, color=blue] table [y=y, x=x]{assets/system-bookmarks/final-results/second-experiment-2.dat};
\addplot[smooth, color=blue] table [dashed, y=y, x=x]{assets/system-bookmarks/final-results/second-experiment-3.dat}; \addplot[smooth, color=DarkGreen] table [dashed, y=y, x=x]{assets/system-bookmarks/final-results/second-experiment-3.dat};
\end{axis} \end{axis}
@ -160,7 +160,7 @@ One should note that the difference between the two policies can be described in
Figure~\ref{sb:psnr-second-experiment} shows the evolution of the PSNR along time during the second experiment (bookmark guided tour), averaged over all users. Figure~\ref{sb:psnr-second-experiment} shows the evolution of the PSNR along time during the second experiment (bookmark guided tour), averaged over all users.
Below the PSNR curve is a curve that shows how many users were moving to or staying at a bookmark position at each point in time. Below the PSNR curve is a curve that shows how many users were moving to or staying at a bookmark position at each point in time.
As we can see, the two policies have a similar performance at the beginning when very few users have clicked bookmarks. As we can see, the two policies have a similar performance at the beginning when very few users have clicked bookmarks.
This changes after 10 seconds, when most users have started clicking on bookmarks. A performance gap grows and the optimized policy performs better than the greedy policy. It is natural to observe such performance, as it reflects the fact that the optimized policy makes better decisions in terms of PSNR (as previously shown in Figure~\ref{sb:precomputation}. This probably explains the previous result in which users tend to prefer the optimized policy. This changes after 10 seconds, when most users have started clicking on bookmarks. A performance gap grows and the optimized policy performs better than the greedy policy. It is natural to observe such performance, as it reflects the fact that the optimized policy makes better decisions in terms of PSNR (as previously shown in Figure~\ref{sb:precomputation}). This probably explains the previous result in which users tend to prefer the optimized policy.
Figure~\ref{sb:psnr-second-experiment-after-click} shows the PSNR evolution after a click on a bookmark, averaged over all users and all clicks on bookmarks. Figure~\ref{sb:psnr-second-experiment-after-click} shows the PSNR evolution after a click on a bookmark, averaged over all users and all clicks on bookmarks.
To compute these curves, we isolated the ten seconds after each click on a bookmark that occurs and we averaged them all. To compute these curves, we isolated the ten seconds after each click on a bookmark that occurs and we averaged them all.
@ -182,8 +182,7 @@ The p-value for statistical significance of this observed difference is 0.06 whi
\midrule Greedy & 4 & 1 & 1 & 1 & 3 & 3 & 1 & 7 & 6 & \textbf{3}\\ \midrule Greedy & 4 & 1 & 1 & 1 & 3 & 3 & 1 & 7 & 6 & \textbf{3}\\
Bookmark & 3 & 5 & 2 & 5 & 10 & 7& 6& 4& 6 & \textbf{5.33}\\ \bottomrule Bookmark & 3 & 5 & 2 & 5 & 10 & 7& 6& 4& 6 & \textbf{5.33}\\ \bottomrule
\end{tabular} \end{tabular}
\caption{Number of click on bookmarks on the last experiment} \caption{Number of click on bookmarks on the last experiment\label{sb:table-bookmark-clicks}}
\label{sb:table-bookmark-clicks}
\end{table} \end{table}
Table~\ref{sb:table-bookmark-clicks} illustrates the number of bookmark clicks for each user (note that distinct users did this experiment on greedy and optimized policy). As we can see, all users clicked at least once on a bookmark in this experiment, regardless of the policy they experiences. However, in the greedy policy setup, 4 users clicked only one bookmark whereas in the optimized policy setup, only one user clicked less than three bookmarks. Table~\ref{sb:table-bookmark-clicks} illustrates the number of bookmark clicks for each user (note that distinct users did this experiment on greedy and optimized policy). As we can see, all users clicked at least once on a bookmark in this experiment, regardless of the policy they experiences. However, in the greedy policy setup, 4 users clicked only one bookmark whereas in the optimized policy setup, only one user clicked less than three bookmarks.