## 5 Experimental Results
### Object Tracker Performance  
Object tracking is also an important part of our proposed authoring system. It generated narratives from 360\({}^{\circ}\) video based on arbitrary object tracking for delivering the experiences in VR. We computed the precision P; recall R, and F-measure to evaluate the performance of the tracker. The calculation formula for F-measure is F = 2 PR/(P + R). Recall is the total true positives divided by the number of occurrences, which should have been detected, while precision is calculated as the number of true positives divided by the number of all responses given by the tracker [32]. The desired object within the frame was considered correctly detected if the detected region of the frame covers 50% of the target object [32]. For validation of our proposed method, we used eight 360\({}^{\circ}\) panoramic videos, which consisted of more than 15,000 frames. These videos were taken from multiple open source video channels on YouTube. The videos included multiple objects, which were desirable for tracking, such as a pedestrian, snorkeler, snowboarder, airplane cockpit, etc. as shown in Figure 9, with correctly detected objects and successful tracking. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Statement**} & **Strongly** & \multirow{2}{*}{**Disagree**} & \multirow{2}{*}{**Neutral**} & \multirow{2}{*}{**Agree**} & **Strongly** & **Average** \\  & **Disagree** & & & & **Agree** & **Score** \\ \hline
**Score** & **1** & **2** & **3** & **4** & **5** & **-** \\ \hline
**Usability** & & & & & & \\ \hline The interface of the system to parse video is easy to understand & 0 & 0 & 1 & 13 & 11 & 4.4 \\ Tasks such as previewing and editing the & & 0 & 1 & 11 & 13 & 4.48 \\ recorded experience are easy to perform & & 0 & 2 & 11 & 12 & 4.4 \\ Convenient to use & & 0 & 2 & 6 & 17 & 4.6 \\ It does not take a very long time to learn the & & 0 & 0 & 6 & 17 & 4.6 \\ system and perform operations & & & & & & \\ \hline Overall Results for Usability & 0\% & 0\% & 6\% & 41\% & 53\% & - \\ \hline
**Efficiency** & & & & & & \\ \hline When you previewed your experience, the & & & & & & \\ \multicolumn{1}{l}{recorded experience was similar to the actual one} & 0 & 0 & 0 & 7 & 18 & 4.72 \\ Knowing that your experience is being & & & & & & \\ \multicolumn{1}{l}{recorded while watching a 360\({}^{\circ}\) video does not restrict enjoyment} & 0 & 0 & 5 & 4 & 16 & 4.44 \\ The experience with the system was smooth & & & & & & \\ \multicolumn{1}{l}{overall and performing operations such as parsing a video did not cause long delays in watching the video} & 0 & 0 & 1 & 7 & 17 & 4.64 \\ \hline Overall Results for Efficiency & 0\% & 0\% & 8\% & 24\% & 68\% & - \\ \hline
**Satisfaction** & & & & & & \\ \hline You can record your 360\({}^{\circ}\) video experience & & & & & & \\ \multicolumn{1}{l}{with very little knowledge about creating & 0 & 0 & 4 & 21 & 4.84 \\
360\({}^{\circ}\) video experiences & & & & & & \\ \multicolumn{1}{l}{You will recommend this system to others} & 0 & 0 & 0 & 7 & 18 & 4.72 \\ \hline Overall Results for Satisfaction & 0\% & 0\% & 0\% & 22\% & 78\% & - \\ \hline
**Overall Result** & & & & & & & 4.58 (91.6\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall results of user survey. Figure 9a shows the example of successful object re-identification in the case of multiple candidate objects; Figure 9b shows that tracking is successful even with a small part of the object visible; Figure 9c represents successful object tracking in case of occlusion; and Figure 9d shows the tracker's performance for object re-identification when the object exits from one side and appears on the other side of the 360\({}^{\circ}\) panoramic frame. Table 2 shows the tracker's performance in terms of precision, recall, and F-measure for each 360\({}^{\circ}\) panoramic video. If we analyze the information provided in Table 1, the precision measure was higher than the recall measure.