 Hand detection and tracking are important components in many computer vision applications, such as hand pose estimation and gesture recognition for human computer interaction systems, virtual reality, and augmented reality. Despite their significance, reliable hand detection in cluttered scenes remains a challenge. To address this issue, a new algorithm was developed which combines the kernelized correlation filter, KCF, tracker with the single shot detection, SSD method. This combination enables the detection and tracking of hands in difficult environments, such as cluttered backgrounds and occlusion. When the KCF tracker fails or experiences drift issues due to sudden changes in hand gestures or rapid movement, the SSD algorithm can be used to reinitialize the KCF tracker. Testing in challenging scenarios demonstrated that the proposed tracker achieved a tracking rate of over 90 percent, while maintaining a speed of 17 frames per second FPS. Additionally, the proposed method outperformed the KCF tracker and the MediaPie Pan tracker in terms of overall tracking detection rate, TRDR, and tracking speed. These results suggest that the proposed method has great potential for long-sequence tracking stability, reducing drift issues, and This article was authored by Mohammed Norzali Haji Mohammed, Mohammed Sharmi Mohammed Asari, Ong Le Ping, and others. We are article.tv, links in the description below.