Abstract: The classical visual single-target tracking method usually describes the tracking target with a single feature. But in the actual scene, the target is changed by the external factor such as light or its own changes such as deformation. In order to better describe the target, the HOG feature and CN feature are introduced firstly, and the traditional feature extraction method is used to train the respective correlation filter, and then the response graph is obtained by correlating filter with the respective feature. Finally, the weights of each response graph are obtained by using the difference method between actual response and expected response, and the final position of the target is obtained by adaptively fusion with each response graph, and their models are updated adaptively. The experiment selected 34 color video frame sequences from the common data set OTB 2013 to analyze and prove the different algorithms qualitatively and quantitatively. Compared with the best DSST algorithm, the average center error is reduced by 7.8 pixels. The success rate is improved by 1.2% and the accuracy is improved by 2.3%. The experimental results show that the algorithm has good tracking robustness and accuracy.

Key words: target tracking, correlation filter, weight, feature fusion, model adaptive update