To solve the problem of poor robustness and low effectiveness of target tracking in complex scenes, a target tracking algorithm based on adaptive multi-feature fusion in tracking-by-detection framework is proposed. Features are extracted from the sub-images extracted by dense sampling, and the target appearance models are established respectively. The response of each model is obtained with regularized least squares classifier. The final response is achieved by weighted sums of the responses, in which the weights are updated by solving a regression equation. It helps to obtain accurate and stable detection scores by enhancing local discrimination. Experimental results show that the algorithm outperforms other state-of-the-art tracking algorithms in tracking accuracy and robustness in most complex scenes.