Aiming at the limited advantage of deep feature tracking and redundancy in computing and storage in the relevant filter tracking framework,a multi-scale estimation and adaptive response fusion target tracking algorithm was proposed.In the algorithm,the advantages of accuracy and robustness of manual features and depth features could be full used by adjusting the parameters of Gaussian label,and the continuous domain convolution operators were learned to fuse multi-resolution features;in order to reduce the redundancy of computation and samples,the feature was supervised and reduced by deconvolution operation to reduce the model parameters.The dynamic sample fusion based on Gaussian mixture model was adopted,and the model updating mechanism with sparse paste improved the effectiveness of the model,and the adaptive response fusion was adapted according to the prediction quality evaluation standard.The experimental results show that the algorithm has good tracking efficiency in many cases such as occlusion,deformation and fast motion.