## 2 Related Works and Motivation  
Vision-based marker-less hand tracking holds great promise for HCI applications, including sign language recognition, augmented reality, telesurgery, home automation, and gaming. However, its implementation faces several challenges, including tracking inaccuracy caused by complex articulated hand motion, high appearance variability, and demanding computational and real-time requirements [10; 11; 12; 13; 14]. Consequently, research in this field continues to be a challenging problem that has gained significant attention from the computer vision community. [PERSON] et al. [11] presented a real-time hand tracking system based on a single depth camera. The system tracked hands in various poses and environments and demonstrated fast and accurate recovery from tracking failures. They utilized the analysis by synthesis technique, generating candidate poses and scoring them against the input image to determine the most likely hand pose. Machine learning and temporal propagation contributed to a large set of candidate hand-pose hypotheses. The evaluation showed accurate tracking of different hand poses, including open, closed, and gesturing hands. However, the system had limitations regarding occlusions and changes in lighting conditions. Recent studies have shown that the accuracy of vision-based tracking can be improved by the adoption of deep learning methods through the Convolutional Neural Network (CNN) technique. [PERSON] et al. [15] proposed an innovative real-time 3D hand tracking method using monocular RGB images. Their approach combined a CNN with a kinematic 3D hand model to achieve accurate tracking. They predicted 2D joint heatmaps, enabling the 3D hand model to infer corresponding 3D joint positions. A noteworthy contribution was the introduction of a novel synthetic data generation method using a Generative Adversarial Network (GAN). This GAN was trained to generate hand images that closely resemble real images, enhancing the CNN's robustness to variations in lighting, pose, and occlusion. However, the authors acknowledged certain limitations in their approach. They addressed scenarios where the hand and background had similar appearances, which posed challenges for their network model, leading to unstable tracking and inaccurate predictions. Furthermore, tracking became problematic when multiple hands were in close proximity within the input image, resulting in unreliable detections and posing a challenge for accurate tracking. [PERSON] et al. [16] proposed MediaPipe hands, a real-time hand detection and tracking system based on the BlazePalm CNN architecture. This method accurately predicted hand poses using image inputs and detected hand bounding boxes and landmarks. The system followed a \"tracking by detection\" approach, with the palm detector locating hand regions and the hand landmark model estimating precise 2.5D landmarks. These landmarks accurately depicted specific key points on the hand, such as fingertips, knuckles, and the center of the palm. However, challenges arose when hands were occluded, in extreme poses, or appeared very small in the image due to large distances. Additionally, factors such as lighting conditions and cluttered backgrounds could affect system performance. MediaPipe hands offered robust hand tracking with accurate pose estimation, but limitations had to be considered. The CNN sub-libraries such as Regional-based Convolutional Neural Network (R-CNN), Faster R-CNN, You Only Look Once (YOLO), and Single-Shot Detection (SSD) algorithms have also gained attention in this research field. In [PERSON] et al. [8], hand pose estimation was achieved by performing skeleton-difference network (SDNet) analysis to predict the locations of hand joints. To train the network for detecting hand location, a deep learning model based on CNN was implemented. For model training, the dataset was pre-processed by cropping the hand region and labelling the hand joints.