To solve the low rate of accuracy, real-time and robustness of object tracking algorithm based on model updating, a new algorithm based on deep residual features and entropy energy optimization was proposed. Deep residual features were first extracted from original video sequence by deep residual network. The entropy energy from deep residual features were calculated, and the deep frequency from entropy energy by two-dimension kernel transformation could be calculated, after that we got the deep balance by deep frequency with differential equation, and then the object state by MLE was estimated, including object position and speed. To validate the feasibility and efficiency of the proposed algorithm, the comparing experiments on the object tracking basis (OTB) dataset for the state-of-the-art algorithms were done, and the comparison results showed that the proposed algorithm had significant improvement on tracking accuracy and robustness. By using entropy energy optimization for deep residual features, the proposed algorithm had more flexibility and robustness for object tracking.
