In general, effective integrating the advantages of differenttrackers can achieve unified performance promotion. In this work, we studythe integration of multiple correlation filter (CF) trackers; propose a novelbut simple tracking integration method that combines different trackers infilter level. Due to the variety of their correlation filter and features, there isno comparability between different CF tracking results for tracking integration.To tackle this, we propose twofold CF to unify these various responsemaps so that the results of different tracking algorithms can be compared,so as to boost the tracking performance like ensemble learning. Experimentof two CF methods integration on the data sets OTB demonstrates that theproposed method is effective and promising