Multiple Features Tracking Algorithm Based on an Improved Fusion Strategy

To solve the problem of visual tracking by multiple features fusion in complex scenes, a multiple features tracking algorithm based on an improved fusion strategy is presented. The proposed algorithm can solve the common computational problem and dimensional fault of the fusion algorithm by the improved fusion method under the particle filter framework. State relationship diagram introduced in the novel fusion strategy can improve the precision and robustness of tracking. When there are deviations of object state, this method can estimate the uncertainty measurement of features by sparsity and select the best state on line. In this way, the quality of state space can be guaranteed, and the performance of object tracking is improved. Experimental results show that the proposed method is stabler and more robust than the single feature algorithm and other multiple features fusion tracking algorithms. The root mean square errors in 3 experiments are all less than 1.2 pixels.