Aiming at the problem that the target resolution in unmanned aerial vehicle (UAV) tracking scene is low and it is difficult to track the target robustly due to the influence of UAV flight attitude,speed change and other factors,an adaptive spatial-temporal regularization algorithm for UAV target tracking is proposed.Based on the spatial temporal regularized correlation filter (STRCF) algorithm,the cost of spatial regularity in AutoTrack is introduced,and the spatial-temporal regularization parameters are dynamically updated online by using the peak side lobe ratio and local response variation vector to improve the accuracy of the tracker.Occlusion processing module is embedded in the tracker to solve the tracking drift problem after the target is occluded.Experimental results on several UAV benchmark datasets show that the proposed algorithm has higher accuracy and faster processing speed than the benchmark algorithm Autotrack.The tracking accuracy and speed of DTB70 data set are improved by 1.5% and 74.4% respectively.In the classification comparison of nine attributes on UAVDT data set,this algorithm achieves high performance on seven attributes,such as scale variation (SV),object blur (OB),etc.It can be seen that this algorithm can better meet the requirements of UAV applications.