Abstract: In order to solve the tracking drift problem caused by the correlation filtering visual tracking algorithm in complex scenes, a correlation filtering tracking framework fused with detection mechanism was proposed.A space-time regularization filter was used as a tracker, and a linear kernel correlation filter was used as a detector.When the response diagram obtained by correlating the tracker with the target was a plurality of peaks, the detector was activated to perform correlation matching on multiple peaks to obtain a retest result; meanwhile, a filter model update strategy using average peak correlation energy was used to obtain a more reliable detector, so as to improve the tracking accuracy and algorithm robustness.The experimental results on the OTB2015, Temple color 128 and VOT2016 data platforms show that compared with the tracking algorithms of better performance proposed in recent years, the proposed algorithm has better robustness and accuracy in complex scenes such as target motion blur, similar background interference and illumination changing, and both of the tracking accuracy and the success rate are improved.