## 4 Result and Discussion
When the hand rapidly moves away from this confusing region, the tracker  
Figure 4: Tracking results of proposed algorithm (red) and KCF tracker (green) on occluded hand behind other object. Figure 3: Tracking results of proposed algorithm (red) and KCF tracker (green) for tracking fast hand movement. loses its tracking position. Nevertheless, the proposed algorithm quickly recovers from this loss by reinitializing the tracking position with the assistance of the SSD algorithm. In contrast, the KCF tracker recovers from the tracking loss much later in the sequence when the hand approaches the previously lost location, relying on the correlation filter mechanism. For the case of tracking deformable hand shapes while drawing gesture \"Q\", the proposed algorithm outperforms the KCF tracker, achieving a higher tracking rate of 98.30% and a relatively lower tracking error of 29.1236. Although both algorithms are able to track the hand accurately with minimal failures, the KCF tracker experiences significant drifting issues, as observed in its tracking results depicted in Figure 6 (Frame 308 and Frame 344). When the hand continuously moves downwards, the bounding box of the KCF tracker drifts and becomes stuck in the background, which shares a similar visual feature with the hand. The KCF tracker fails to recover from this issue throughout the video sequence, resulting in heavy deviations in both the horizontal and vertical axis trajectories and nearly doubling the obtained OTE value compared to the proposed algorithm. In contrast, the proposed algorithm greatly reduces the drifting issue through the error recovery framework facilitated by the integration of the SSD algorithm. To evaluate the generalizability of the proposed approach, a comparison was made with a state-of-the-art tracker, the MediaPipe hand [16]. For this comparison, ten video sequences from the Intelligent Biometric Group Hand Tracking (IBGHT) dataset [34] were used. This dataset was chosen for the experiment as it provides dynamic hand trajectories along with ground truth data. Table 4 provides detailed information about each sequence. Table 5 presents a summary of the tracking results obtained by the proposed method and the MediaPipe hand tracker on ten trajectory-based hand gestures from the IBGHT dataset. The proposed method achieved an average target detection and tracking rate (TRDR) of 94.38% across all gestures, surpassing the performance of the MediaPipe hand tracker, which achieved 93.06%. However, it is worth noting that the MediaPipe hand tracker exhibited a lower object tracking error (OTE) with an average value of 7.59, compared to 10.59 for the proposed method. This indicates that the MediaPipe hand tracker excelled in accurately localizing the hand objects, resulting in fewer errors in object boundaries and position estimation. Despite the higher OTE, the proposed method demonstrated superior tracking speed, achieving an average frames per second (FPS) of 17.8, compared to the MediaPipe hand tracker's 12.7 FPS. The higher TRDR achieved by the proposed  
Figure 5: Tracking results of proposed algorithm (red) and KCF tracker (green) on outdoor scene. method highlights its effectiveness in detecting and tracking hands in the given video sequences, even in challenging environments with cluttered backgrounds and occlusions. In conclusion, the proposed KCF + SSD method outperforms the MediaPipe hand tracker in terms of overall tracking accuracy (TRDR) and tracking speed (FPS). However, it is important to consider that the MediaPipe hand tracker excels in precise localization of hand objects, leading to lower object tracking errors (OTE). Therefore, the choice between the two methods depends on the specific requirements of the application, considering the trade-off between accuracy and speed. Further optimizations can be explored to improve the proposed method's OTE while maintaining its superior tracking performance.