Aimed at the problem that the Kernelized Correlation Filter position of the target is in the image through the correlation filter，such generative models are easily subject to the interference from the backgrounds similar to the targets, causing the failure of tracking, this paper strengthens the criterion of correlation filter based on the maximum margin. The similar backgrounds are updated as negative samples to improve the tracking robustness. First, the algorithm constructs a model of maximum margin correlation and distinguishes backgrounds similar to the targets by classifier. Second，the obtained similar backgrounds are taken as negative samples and the tracking model is updated online during the tracking process. So the algorithm can adapt to the various changes of the target movement and can achieve the goal of robust tracking by online updating the target tracking model. The paper selects seventeen typical image sequences of OTB2013 and VOT2014 database and compares the results of the six correlation tracking algorithm in the experiment. The experimental results show that the algorithm can improve by 8% and by 2% on precision and success rate compared with the suboptimal algorithm, the tracking efficiency is the best and the tracking realtimeability is comparatively good.