This study introduces an enhancement approach for single object tracking (SOT) algorithms, aimingto use temporal information with minimize architectural changes while preserving the original modeldimensions with minimal impact on the speed. Most state-of-the-art approaches prioritize effectivefeature extraction from the object or the search window images, while temporal information is oftenneglected. Additionally, many deep models used in SOTs are time-consuming compared to traditionaltrackers. This study focuses on SwinTrack V2 as our case study due to its absence of temporalinformation utilization and its excellent performance on GOT-10k dataset. The method suggest addinga third input to the model, which is an updated object template. The initial template features arefused with the updated template using an attention block, which effectively preserves the originalmodel dimension. Our additional block improves performance when testing on Got-10k dataset by 0.9% for average overlap AO and by 2.1% for success rate SR0.5 compared to the original tracker,with only a slight impact on speed.