# Visual Tracking Using Wang-Landau Reinforcement Sampler
## 4 Experiments
### Quantitative Comparison  
Figure 2 shows the quantitative comparisons with non-deep learning-based visual trackers. Our method considerably outperformed the second-best trackers, MEEM and Staple, in terms of precision and success rate, respectively. Our method was able to accurately track the target despite the severe appearance of the target. In particular, the proposed Wang-Landau Monte Carlo sampling improves the exploration of unvisited states, enabling our tracker to cover abrupt motion changes of the target. Figure 3 shows the quantitative comparisons with recent deep-learning-based visual trackers. SiamDW was the best in terms of the precision plot, and ECO was the best in terms of the success plot. As shown in Figure 3, our method was competitive with deep learning-based visual trackers. In particular, our method produced accurate tracking results in terms of the success plot but relatively inaccurate tracking results in terms of the precision plot, implying that our method can be improved by adopting multiscale approaches. In Figure 4, we highlighted experiments on test sequences, which contain examples of interrupted and recovered tracking. For example, \"Out of view\" and \"Occlusion\" sequences contain interrupted and recovered tracking scenarios. In these sequences, target objects frequently disappear due to occlusion and out of view attributes, which causes conventional trackers to miss the target trajectories. After a long time, the targets reappear and the trackers need to recover the target trajectories. In this situation, the proposed tracker efficiently recovers missing trajectories using the proposed exploration mechanism. As shown in Figure 4, the propose visual tracker considerably outperforms other state-of-the-art deep-learning visual trackers, which demonstrates the effectiveness of the proposed exploration mechanism based on Wang-Landau sampling. Figure 5 quantitatively evaluates the proposed method (ours) and the recent state-of-the-art deep-learning-based visual trackers using the VOT2014 dataset. Our method considerably outperforms \begin{table}
\begin{tabular}{c c c} \hline  & \(\epsilon\)**-Greedy** & **Wang–Landau Sampling** \\ \hline AUC & 0.583 & 0.694 \\ \hline \end{tabular}
\end{table} 
Table 1: Ablation study: Effectiveness of Wang–Landau sampling for reinforcement learning-based visual tracking. \begin{table}
\begin{tabular}{c c c c} \hline \(\gamma\) & 0.8 & 0.9 & 1.0 \\ \hline
**AUC** & 0.651 & 0.694 & 0.647 \\ \hline \(N\) & 1000 & 2000 & 3000 \\ \hline
**AUC** & 0.642 & 0.694 & 0.649 \\ \hline \(w\) & 0.7 & 0.8 & 0.9 \\ \hline
**AUC** & 0.650 & 0.694 & 0.637 \\ \hline \end{tabular}
\end{table} 
Table 2: Ablation study: Sensitivity to hyperparameters, \(\gamma\) in (8), \(N\) in Algorithm 1, and \(w\) in (12). other methods in terms of accuracy. Staple shows the second-best accuracy. However, robustness is significantly worse than the proposed method, implying that our method rarely missed the targets, while preserving the accuracy over frames. Gnet is the best in terms of robustness, while its accuracy is lower than ours. Table 3 quantitatively evaluates the proposed method and deep-learning-based visual tracking methods using the LaSOT dataset. Our method and GlobalTrack present state-of-the-art tracking performance. GlobalTrack has an explicit redetection, which requires accurate object detectors. In contrast, the efficient performance of the proposed method stems from reinforcement learning with Wang-Landau-based exploration. Our method implicitly searches for the targets without any object detector. Table 4 quantitatively evaluates the computational costs of recent visual trackers using the LaSOT dataset. Our visual tracker shows state-of-the-art performance in terms of FPS and can run in realtime because our method contains no complicated deep neural network architectures. Figure 6 measured the recovery rated for 8 state-of-the-art visual trackers, namely ECO, SiamRPN++, GlobalTrack, ATOM, DASiam, CFNet, SPLT, and StructSiam using the LaSOT dataset.