Visual object tracking is a crucial part of computer vision, involving extracting features, integrating target information, and estimating object bounding boxes. Despite numerous tracking algorithms, few are optimized for practical use. This research addresses this gap by analyzing the properties of a high frame rate dataset called NFS and introducing a Stochastic Model to enhance video object tracking. Our contributions are as follows: (a) We have proposed for the first time a stochastic model that describes failures in visual object tracking tasks. (b) We introduced a mathematical hypothesis for the visual object tracking process based on a Poisson process model, and validated the model’s effectiveness through our statistical analysis. (c) Among the current state-of-the-art trackers, our method demonstrates excellent performance in both speed and accuracy, making it one of the fastest trackers. We believe that this innovative model will significantly enhance the practicality and application prospects of visual object tracking technology.