Video tracking is one of the fundamental and widely used topics in the field of computer vision, with applications ranging from surveillance and security systems to autonomous vehicles and human-computer interaction. Despite extensive research and significant advancements in this area, numerous challenges continue to affect the performance and reliability of visual tracking systems. Among these challenges are occlusion, where objects are partially or completely hidden from view; lighting changes, which can alter the appearance of objects; scale and size variations; rapid object movements; background clutter; and computational complexity, which hinders real-time processing capabilities. In this paper, a particle filter is employed as the motion model to predict and estimate the states of the target object over time. The observation model evaluates the likelihood of the predicted states based on their correlation with a predefined pattern or template. By leveraging correlation measurement for state evaluation, the proposed video tracking method can address several of the common challenges, such as changes in lighting and partial occlusion. Moreover, because correlation measurement typically requires lower computational resources compared to more complex tracking algorithms like deep learning-based methods, the proposed approach enables faster processing speeds. Consequently, it offers improved performance for real-time applications where both accuracy and efficiency are critical.