In order to reduce the poor effect of illumination,occlusion,size change and other factors on the target tracking process,it was proposed to increase the occlusion detection and processing mechanism on the basis of classical spatial regularization correlation filtering.The similarity calculation and spatial distance calculation were used as the criterion of occlusion detection.If the occlusion existed,the occlusion process was performed,that is,the update of the model was stopped;otherwise,the model was updated.Secondly,the radius selection was proposed,and the target tracking was carried out with six search radius,and the optimal search radius was found.Then,it was proposed the feature selection method,and the feature of HOG,PHOG,Haar,LBP and FHOG were combined with the algorithm to select the best features.Two groups of experiments were used for verification: classical KCF algorithm,Mean Shift algorithm,Fragment algorithm,DSST algorithm,classical SRDCF algorithm and improved SRDCF algorithm were used to track and compare moving targets in Bolt2 and Basketball.The experimental results show that FHOG feature combined with the improved spatial regularized correlation filtering has the best tracking performance when the search radius is 8,and it is superior to other classical tracking algorithms.The processing speed can reach 3.7 fps.