摘要：Neutrosophic set (NS) is a new branch of philosophy to deal with the origin, nature, and scope of neutralities. Many kinds of correlation coefficients and similarity measures have been proposed in neutrosophic domain. In this work, by considering that there may exist different contributions for the neutrosophic elements of T (Truth), I (Indeterminacy), and F (Falsity), a method of element-weighted neutrosophic correlation coefficient is proposed, and it is applied for improving the CAMShift tracker in RGBD (RGB-Depth) video. The concept of object seeds is proposed, and it is employed for extracting object region and calculating the depth back-projection. Each candidate seed is represented in the single-valued neutrosophic set (SVNS) domain via three membership functions, T, I, and F. Then the element-weighted neutrosophic correlation coefficient is applied for selecting robust object seeds by fusing three kinds of criteria. Moreover, the proposed correlation coefficient is applied for estimating a robust back-projection by fusing the information in both color and depth domains. Finally, for the scale adaption problem, two alternatives in the neutrosophic domain are proposed, and the corresponding correlation coefficient between the proposed alternative and the ideal one is employed for the identification of the scale. When considering challenging factors like fast motion, blur, illumination variation, deformation, and camera jitter, the experimental results revealed that the improved CAMShift tracker performs well.
