# Comparison of Tracking Techniques on 360-Degree Videos
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**TrackersFeatures** & **Principle** & **Strength** & **Weakness** &
\begin{tabular}{c} **Improvement** \\ **Suggestion** \\ \end{tabular} \\ \hline
**GOTURN** & Pretrained CNN & \begin{tabular}{c} Recovery from \\ failure and \\ occlusion \\ \end{tabular} & \
\begin{tabular}{c} Target not in \\ training data \\ \end{tabular} &
\begin{tabular}{c} Include specific \\ targets for training in \\ advance \\ \end{tabular} \\ \hline
**MedianFlow** & \begin{tabular}{c} Min \\ forward–backward \\ error \\ \end{tabular} & \begin{tabular}{c} Reliable on slow \\ changing target \\ \end{tabular} & \
\begin{tabular}{c} Fast-moving \\ target \\ \end{tabular} &
\begin{tabular}{c} Support motion \\ detection \\ \end{tabular} \\ \hline
**TLD** & \begin{tabular}{c} Track, learn, and \\ detect \\ \end{tabular} & \begin{tabular}{c} Recovery from \\ failure and \\ occlusion \\ \end{tabular} & \
\begin{tabular}{c} High false alarm \\ \end{tabular} &
\begin{tabular}{c} Combine with \\ reliable filter \\ \end{tabular} \\ \hline
**KCF** & \
\begin{tabular}{c} Kernelized \\ correlation filter \\ \end{tabular} &
\begin{tabular}{c} Report tracking \\ failure \\ \end{tabular} & Fixed target size & Adaptable target size \\ \hline
**BOOST** & AdaBoost & \
\begin{tabular}{c} Decent accuracy \\ \end{tabular} &
\begin{tabular}{c} Seldom report \\ tracking failure \\ \end{tabular} & Adaptable tolerance \\ \hline
**CSRT** & \begin{tabular}{c} Discriminative \\ correlation filter \\ \end{tabular} & \begin{tabular}{c} Robust and high \\ accuracy \\ \end{tabular} & \
\begin{tabular}{c} Long-term \\ occlusion \\ \end{tabular} &
\begin{tabular}{c} Failure recovery with \\ spatial relationship \\ \end{tabular} \\ \hline
**MIL** & \begin{tabular}{c} Multi-instance \\ learning \\ \end{tabular} & High accuracy & \
\begin{tabular}{c} Long-term \\ occlusion \\ \end{tabular} &
\begin{tabular}{c} Failure recovery with \\ spatial relationship \\ \end{tabular} \\ \hline
**MOSSE** & Min square error &
\begin{tabular}{c} High tracking \\ speed \\ \end{tabular} & Fixed target size & Adaptable target size \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Strengths and weaknesses of all eight trackers on 360-degree videos. trackers must learn and update the appearance model in run-time. To accommodate lighting change, extracting illumination-robust features is critical. To solve the problem of scale change, adaptable and dynamic target window size is beneficial. To deal with a shaking camera, trackers should measure and compensate the global motion. An inherent problem of 360-degree videos in an equirectangular format is the image distortion, especially for the northern and southern polar areas in a panoramic image. The distortion problem affects the performance of tracking in two aspects. First, the motion of the tracking target is distorted after an equirectangular projection [22]. In video sequence 6, a boat moving in a straight line looks like it is moving in a curve in the 360-degree video in an equirectangular format. The distortion of trajectory degrades the accuracy of all trackers, especially for fast-moving targets. Nevertheless, the GOTURN tracker can handle this problem properly as long as it includes the training samples with distorted motions in the process of pretraining. Second, the tracking target suffers from nonrigid deformations in an equirectangular format. In video sequence 2, the deformation of the target building makes straight lines become curves. Thus, it tends to cause a track loss for trackers using a static target appearance model. Surprisingly, most trackers survive the slow target deformation in this case except the TLD tracker. The deformed target triggers frequent reinitialization in the TLD modules, and the target appearance is prone to drift in the presence of occasional occlusions. In video sequence 3, the tracking target is accompanied by both motion distortion and target deformation. As a result, most trackers are unstable and achieve low tracking accuracy in this case.