Multi-feature fusion robust particle filter tracking based on fuzzy measure

In order to overcome the problem that particle filter tracking based on the single color feature is susceptible to illumination changes, partial occlusion and the interference of the similar, and the feature weight, tracking template and tracking window size are difficult to adaptive when the particle filter tracking method based on multi-feature fusion is used, a multi-feature fusion particle filter tracking based on the fuzzy measure is presented. A color histogram and a edge orient histogram are used to describe the target measure model, and Bhattacharyya distance of these two features between the candidate and reference targets is used to determine their separate fuzzy measures. Then, the weights of these two features are adaptively determined by referring to the fuzzy rule table. Besides, a combined template update mechanism of multi-feature based on successive frames is adopted to update the initial target template. Finally, particle dispersion is introduced to solve the problem that the tracking window cannot adapt to changes of the tracking target scale. Experimental results indicate that the average error of the proposed tracking algorithm is less than 8 pixel errors. Compared with the traditional tracking algorithm, the proposed algorithm can effectively solve the problem of illumination changes, partial occlusion and the interference of the similar, and it can satisfy the system requirements of higher precision and strong robustness.