## 4 Result and Discussion
In the case of tracking slow hand movements, both the proposed algorithm and the KCF tracker exhibit similar performance. However, when it comes to handling fast and abrupt hand movements, the proposed algorithm outperforms the KCF tracker. The proposed algorithm achieves a higher tracking rate of 84.01%, which is twice that achieved by the KCF tracker. This indicates that the proposed algorithm is more effective at accurately tracking the hand during fast movements. While the proposed algorithm does have a relatively higher tracking error (OTE) of 30.341, it demonstrates a lower tendency of tracking failure compared to the KCF tracker. This can be observed in Figure 3, specifically in Frame 209 and Frame 309, where the proposed algorithm successfully keeps up with the fast-moving hand while the KCF tracker encounters tracking failure issues and fails to reinitialize its system. Furthermore, it was noted that the trajectory plots obtained from the proposed tracker are more complete compared to the implementation of the KCF tracker alone. It is important to acknowledge that, although the proposed algorithm tracks the hand object throughout a larger number of frames, offset errors between the estimated position and the ground truth tend to propagate. On the other hand, the KCF tracker fails to track the hand much earlier, resulting in lesser propagation of offset errors. In the case of an occlusion-handling scenario, although the tracking rate of the proposed algorithm is relatively lower, its accuracy surpasses that of the KCF tracker, resulting in a lower tracking error. By examining the tracking results in Figure 4, it becomes apparent that both the horizontal and vertical axis trajectories of the KCF tracker deviate significantly from the ground truth due to tracking failures and severe drifting issues. Despite the KCF tracker managing to track the heavily occluded hand, its bounding box drifts towards the occluded object and fails to recover even when the hand reappears in that region. In contrast, the proposed algorithm demonstrates the ability to recover from drifting issues by reinitializing the system, as illustrated in Frame 142 to Frame 160 in Figure 4. Although tracking a heavily occluded hand remains challenging, the overall performance of the proposed tracker in handling occlusion has shown significant improvement. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Tracking Scenes**} & \multirow{2}{*}{ \begin{tabular}{c} **Frame** \\ **Num.** \\ \end{tabular}  } & \multicolumn{2}{c}{**KCF + SSD (Proposed)**} & \multicolumn{2}{c}{**KCF**} & \multicolumn{2}{c}{**Improvement (\%)**} \\ \cline{3-10}  & & \ \begin{tabular}{c} **TRDR** \\ **(\%)** \\ \end{tabular}  & **OTE** & **FPS** & \begin{tabular}{c} **TRDR** \\ **(\%)** \\ \end{tabular}  & **OTE** & **FPS** & **TRDR** & **OTE** \\ \hline Slow movement & 546 & 98.26 & 26.609 & 19.0 & 96.32 & 27.607 & 25.0 & 2.014 & 3.613 \\ \hline Fast movement & 664 & 84.01 & 30.341 & 16.0 & 42.97 & 23.893 & 33.0 & 95.509 & \(-\)26.987 \\ \hline Occlusion behind object & 708 & 82.50 & 21.213 & 18.0 & 89.44 & 73.907 & 32.0 & \(-\)7.759 & 71.298 \\ \hline Outdoor scene & 310 & 98.37 & 35.802 & 26.0 & 81.71 & 76.753 & 21.0 & 20.389 & 53.354 \\ \hline Clothes with less contrast to skin tone & 597 & 94.95 & 30.729 & 18.0 & 65.16 & 26.383 & 27.0 & 45.718 & \(-\)16.472 \\ \hline Deformable hand & \multirow{3}{*}{384} & \multirow{3}{*}{98.30} & \multirow{3}{*}{29.123} & \multirow{3}{*}{16.0} & \multirow{3}{*}{95.45} & \multirow{3}{*}{59.681} & \multirow{3}{*}{22.0} & \multirow{3}{*}{2.986} & \multirow{3}{*}{51.201} \\ \cline{3-3} \cline{5-5} \cline{5-5} \  
In the case of outdoor conditions with exposed lighting, the proposed algorithm demonstrates significantly better performance compared to the KCF tracker. However, as shown in Figure 5, it can be observed that the tracking bounding box of the proposed algorithm gets stuck in a region with visual characteristics similar to the skin features (Frame 155).