Aiming at the problem of poor tracking by the kernel-related tracking filter algorithm in complex scenes,proposed a correlation filter target tracking algorithm combining depth features and scale adaptation.Firstly,the deep residual network (ResNet) was used to extract the depth features of the tracked area in the image,and then the target area directional gradient histogram feature (FHOG) was extracted,and multiple response maps were obtained through the kernel correlation filter learning,and were performed weighted fusion to obtain the tracking target position.Secondly,a PCA dimensionality reduction scale filter was trained through the directional gradient histogram (FHOG) feature to realize the estimation of the target scale,so that the algorithm had a good adaptive ability to the change of the target scale.Finally,according to the peak fluctuation of the response graph,the model update strategy was improved and the re-detection mechanism was introduced to reduce the probability of model drift and improve the anti-occlusion ability of the algorithm.Compare with other 7 target tracking algorithms in the standard data set OTB100.The experimental results show that the accuracy of the original KCF algorithm is improved by 29.3%,and the success rate is improved by 25.3%.The proposed algorithm achieves accurate estimation of tracking target position,improves the scale adaptive ability and the speed of the algorithm and enhances the anti-occlusion ability of the algorithm.