In recent years, there has been a increasing demand for tracking protein molecules with the focus on immune system researches. However, machine learning-based single-particle tracking (SPT) faces the challenges in accuracy due to the rapid and random movement of molecules as well as detection errors. To address these issues, we use frame interpolation to pseudo-decrease the speed of movement and perform two-stage matching to achieve stable tracking. We also use an optimization algorithm that connects short tracks. This approach has achieved higher performance on the CD47 dataset and the PTC dataset than conventional baselines.