# Comparison of Tracking Techniques on 360-Degree Videos
## 4 Discussion  
In terms of Frames Per Second (FPS), Table 2 summarizes the speed comparison of all eight trackers in nine video sequences. According to the experimental results, the MOSSE is the fastest tracker with an average of 3776 FPS. The KCF is the next efficient tracker with an average of 175 FPS. The MedianFlow tracker can also achieve 63 FPS. Among all eight trackers, The TLD is the slowest tracker. In fact, it is about 600 times slower than the MOSSE tracker and not suitable for real-time applications. In terms of tracking quality, Table 3 summarizes the accuracy comparison of all eight trackers in nine video sequences. The strengths and weaknesses of all eight trackers are outlined in Table 4. The GOTURN is the only tracker based on deep learning but does not perform well in terms of accuracy on 360-degree videos. Interestingly, it works well in some special cases. For example, it is the only tracker that could flawlessly track a fast-moving boat in sequence 6, possibly because the pretrained dataset contained boats. In fact, the performance of GOTURN heavily depends on the appearance model of the tracking target. Hence, we believe the GOTURN tracker can be improved by training specific target models for 360-degree videos in advance. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sequences** & **GOTURN** & **MedianFlow** & **TLD** & **KCF** & **BOOST** & **CSRT** & **MIL** & **MOSSE** \\ \hline
**Sequence 1** & 23.7 & 62.8 & 2.6 & 188.9 & 41.6 & 38.9 & 12.8 & 4118.2 \\ \hline
**Sequence 2** & 20.5 & 66.0 & 9.5 & 204.8 & 35.8 & 35.6 & 14.3 & 2250.0 \\ \hline
**Sequence 3** & 16.0 & 61.8 & 2.8 & 172.9 & 26.7 & 32.8 & 14.3 & 2532.2 \\ \hline
**Sequence 4** & 14.3 & 62.8 & 4.9 & 309.5 & 56.9 & 47.6 & 14.9 & 10356.8 \\ \hline
**Sequence 5** & 16.4 & 61.4 & 5.6 & 39.0 & 22.7 & 34.8 & 13.0 & 457.0 \\ \hline
**Sequence 6** & 21.3 & 65.2 & 7.1 & 304.8 & 49.8 & 47.3 & 17.2 & 8614.0 \\ \hline
**Sequence 7** & 21.2 & 63.1 & 8.7 & 41.4 & 15.8 & 22.8 & 12.1 & 440.3 \\ \hline
**Sequence 8** & 23.4 & 63.6 & 8.0 & 104.3 & 23.2 & 31.5 & 12.1 & 1233.8 \\ \hline
**Sequence 9** & 19.3 & 64.1 & 4.9 & 210.8 & 37.4 & 37.8 & 12.7 & 3982.4 \\ \hline
**Average** & 19.6 & 63.4 & 6.0 & 175.1 & 34.4 & 36.6 & 13.7 & 3776.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Speed comparison of eight trackers on nine video sequences in terms of Frames Per Second (FPS). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sequences** & **GOTURN** & **MedianFlow** & **TLD** & **KCF** & **BOOST** & **CSRT** & **MIL** & **MOSSE** \\ \hline
**Sequence 1** & poor & poor & bad & poor & great & great & great & great \\ \hline
**Sequence 2** & great & poor & poor & ok & ok & ok & poor & bad \\ \hline
**Sequence 3** & bad & good & bad & good & good & great & great & great \\ \hline
**Sequence 4** & poor & great & poor & ok & great & great & great & poor \\ \hline
**Sequence 5** & bad & great & great & great & poor & great & great & great \\ \hline
**Sequence 6** & great & ok & ok & bad & ok & good & ok & bad \\ \hline
**Sequence 7** & ok & good & poor & great & great & great & great & good \\ \hline
**Sequence 8** & great & great & great & great & good & great & great & great \\ \hline
**Sequence 9** & bad & great & bad & poor & great & great & great & great \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall accuracy comparison of eight trackers on nine video sequences (bad: 0\(\sim\)20%; poor: 20\(\sim\)40%; ok: 40\(\sim\)60%; good: 60\(\sim\)80%; great: 80\(\sim\)100%). Generally, the MedianFlow tracker performs well on consistent and slowly changing video sequences. However, occasional occlusion prevents it from making an agreement in bidirectional analysis and the tracking fails as shown in video sequence 2. The TLD is a slow tracker with high false detect rate but works well in the case of failure recovery. Especially, the TLD tracker is a good choice to track a target that disappears from one place and reappears in another place in 360-degree videos.