Introducing regularization into the correlation filter tracking algorithm can effectively improve the tracking efficiency,but it takes a lot of effort to adjust the predefined parameters.In addition,the target response occurring in non-target areas will lead to tracking drift.Therefore,an Automatic Global Context Awareness Correlation Filter (AGCACF) tracking algorithm is proposed.First,during the tracking process,the automatic spatial regularization is realized using the target local response change,then its module is added into the target function to enable the filter to focus on the learning of the target object.Second,the tracker utilizes the global context information of the target,which can avail the filter learn more information related to the target and reduce the impact of background on tracking performance. Then a temporal regularization term is added to the filter to fully learn the change of targets between adjacent frames to obtain more accurate model samples.Experimental results show that the proposed AGCACF tracking algorithm has better tracking effect in distance accuracy and success rate compared with other tracking algorithms.