## 5 Conclusions  
A tracking-by-detection algorithm, constructed by integrating the SSD algorithm with the KCF visual tracker, is developed to detect and track hands from color images sequences with cluttered background and exerting minimal constraints on the subject. The proposed algorithm was tested on 17 video sequences and the experimental results show that it is fast and robust for real-time applications, achieving a promising tracking rate of over 90% and overall frame rate of around 17 FPS. Based on the hand detection analysis, it can be justified that the chosen SSD object-detection algorithm is more suitable to be employed in the proposed algorithm due to its higher frame rate and accuracy achieved, as compared to that of the Faster R-CNN algorithm. For hand tracking performance analysis, it can be concluded that the proposed algorithm is able to keep track of the hand seamlessly with a promising tracking rate and lower tracking error. Integration of the SSD algorithm has improved the KCF visual tracker in many aspects, including its long-duration tracking performance, tracking performance during occlusion, and reduced drifting tendency. However, the limitations of KCF tracker such as being unable to effectively track fast-moving hands and heavily occluded hands still remain, due to its intrinsic weakness which is that it solely relies on the single Histogram of Oriented Gradients (HOG) feature extracted during its initialization. In the future, SSD integration with different classes of visual trackers can be explored, such as implementing a Channel and Spatial Reliability Tracking (CSRT) tracker which is well-known for its higher accuracy at the cost of slower computation speed. Although the proposed tracking-by-detection algorithms can reduce local resource computation for hand detection and tracking, the task remains challenging for researchers. Accuracy is often impacted by factors such as occlusion, fast-moving hands, and abrupt gesture changes. Integrating motion-handling features such as color can improve algorithm accuracy and reduce tracking loss. Retraining the SSD algorithm by adding new \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Gestures**} & \multirow{2}{*}{**Frame Num.**} & \multicolumn{2}{c}{**KCF + SSD (Proposed)**} & \multicolumn{3}{c}{**MediaPipe Hand [16]**} \\  & & **TRDR** & **OTE** & **FPS** & **TRDR** & **OTE** & **FPS** \\  & & **(\%)** & **OTE** & **(\%)** & **OTE** & **(\%)** & **OTE** & **FPS** \\ \hline
0 & 244 & 94.26 & 9.463 & 18.0 & 93.9 & 5.465 & 11.0 \\
1 & 238 & 98.3 & 10.781 & 19.0 & 97.9 & 6.871 & 12.0 \\
2 & 237 & 96.6 & 12.389 & 16.0 & 93.3 & 5.878 & 15.0 \\
3 & 229 & 90.3 & 12.516 & 20.0 & 89.52 & 6.875 & 13.0 \\
4 & 210 & 95.2 & 11.032 & 17.0 & 93.8 & 8.456 & 12.0 \\
5 & 222 & 95.5 & 10.725 & 18.0 & 91.4 & 7.678 & 14.0 \\
6 & 242 & 97.5 & 9.950 & 17.0 & 93.4 & 9.447 & 14.0 \\
7 & 221 & 96.8 & 11.102 & 16.0 & 96.4 & 8.890 & 11.0 \\
8 & 242 & 88.8 & 7.388 & 18.0 & 90.1 & 6.475 & 11.0 \\
9 & 242 & 90.5 & 10.599 & 19.0 & 90.9 & 9.864 & 14.0 \\ \hline Average & & 94.38 & 10.59 & 17.8 & 93.06 & 7.59 & 12.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison between the proposed algorithm and MediaPipe hand tracker on ten trajectory-based hand gestures from IBCHT hand tracking dataset [34]. layers for fine-tuning can improve hand detection accuracy and expand the dataset with accurate images. Conceptualization, M.N.H.M., M.S.M.A. and B.A.R. ; Methodology, O.L.P. ; Formal analysis, M.S.M.A. and O.L.P. ; Investigation, O.L.P. ; Resources, B.A.R. ; Writing--original draft, O.L.P. ; Writing--review & editing, M.N.H.M. and M.S.M.A. ; Supervision, M.S.M.A. ; Funding acquisition, M.N.H.M. All authors have read and agreed to the published version of the manuscript. This research is supported by the Ministry of Higher Education Malaysia (MOHE) Fundamental Research Grant Scheme FRGS/1/2019/ICT04/UTHM/02/2, (K187) Universiti Tun Hussein Onn Malaysia and FRGS grant FRGS/1/2020/TK0/USM/02/13, Universiti Sains Malaysia.