## 4 Result and Discussion
The proposed algorithm achieves a lower OTE value of 16.595 and a higher tracking rate of 94.3%. However, in terms of average frame rate, the performance of the KCF tracker surpasses that of the proposed algorithm, with an average speed of 36.3 FPS compared to 16.40 FPS achieved by the proposed algorithm. Although the KCF tracker exhibits faster processing speed, its overall accuracy is comparatively lower. The KCF tracker encounters challenges in accurately tracking hand gestures \"0\", \"3\", \"8\", and \"9\" due to its inflexibility \begin{table}
\begin{tabular}{c c c} \hline \hline
**Mean Average Precision** & **SSD Model** & **Faster R-CNN** \\ \hline mAP (IoU = 0.5:0.95) & 67.74\% & 72.29\% \\ mAP (IoU = 0.5) & 92.22\% & 98.64\% \\ mAP (IoU = 0.75) & 81.71\% & 87.57\% \\ AR (IoU = 0.5:0.95) & 72.7\% & 77.9\% \\ \hline Detection time per images & 0.0667 s & 1.00 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison between SSD and Faster-RCNN for hand detection on EgoHands dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\ \begin{tabular}{c} **Trajectory** \\ **Gesture** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Frame** \\ **Num.** \\ \end{tabular}  } & \multicolumn{2}{c}{**KCF + SSD (Proposed)**} & \multicolumn{4}{c}{**KCF**} & \multicolumn{4}{c}{**Improvement (\%)**} \\ \cline{3-10}  & & **TRDR** & **(\%)** & **PFS** & **(\%)** & **OTE** & **FPS** & **TRDR** & **OTE** \\ \hline
0 & 307 & 96.1 & 32.550 & 17.0 & 87.8 & 79.989 & 31.0 & 9.4077 & 59.3112 \\
1 & 229 & 96.7 & 11.920 & 15.0 & 86.1 & 10.316 & 38.0  
in adapting to scale variations and its susceptibility to drifting issues, especially during rapid hand movements. To provide a deeper understanding of these issues, we direct readers to Figure 2, which presents an example showcasing the tracking results for gesture \"8\". In this case, the proposed algorithm has demonstrated superior performance compared to the KCF tracker, achieving a promising tracking rate of 95.2% and a lower tracking error of 9.02. Conversely, despite the KCF tracker's higher overall frame rate, it exhibits a higher tracking error of 32.3901, which is three times greater than that of the proposed algorithm. Analyzing the tracking results depicted in Figure 2, it becomes evident that the KCF tracker encountered drifting issues, as its bounding box drifted towards the arm when the hand continuously moved upwards, as shown in Frame 202 and Frame 237. On the other hand, the proposed algorithm exhibits excellent tracking results without any drifting issues. Moreover, both the horizontal and vertical axis trajectories of the proposed algorithm closely align with the ground truth trajectories. In contrast, the vertical axis trajectory of the KCF tracker deviates significantly from the ground truth trajectory. The proposed tracking algorithm was further evaluated using several challenging video sequences, including scenarios involving slow and fast hand movements, occlusion behind other objects, outdoor environments, cluttered backgrounds with low contrast to skin tone, and deformable hand shapes while drawing alphabetical trajectory gestures. The evaluation results, along with a comparison to the original KCF tracker and the improvement achieved by the proposed algorithm, are summarized in Table 3. Based on the obtained results, it is evident that the overall performance of the proposed algorithm surpasses that of the KCF tracker. The proposed algorithm achieves a higher average tracking rate of 93.406% and a lower tracking error of 26.199. It is worth noting that the integration of the SSD algorithm has also improved the overall performance of the KCF tracker, resulting in a 22.802% improvement in TRDR and a 24.841% improvement in OTE value. Figure 2: Tracking results of proposed algorithm (red) and KCF tracker (green) for gesture “8”. To gain a deeper understanding of the evaluation results, a qualitative analysis was conducted on specific tracking scenes.