LAUSR

In this paper, a novel intuitionistic fuzzy clustering algorithm based on feature selection (IFC-FS) for multiple object tracking is proposed. In the proposed algorithm, the neighborhood rough set is used to achieve the adaptive selection of the multiple object features of visual objects, which are applied to calculate the distance similarity measure between the objects and the observations. At the same time, in order to incorporate the local information of objects into the intuitionistic fuzzy clustering, the local information distances between objects and observations are estimated by using the optimal subpattern assignment metric based on the reference topology set, and a new intuitionistic fuzzy clustering based on maximum entropy principle is proposed by using the new similarity distance measure. Finally, the association probabilities among the objects and the observations are reconstructed by utilizing the intuitionistic fuzzy membership degrees. The experimental results show that the proposed algorithm can effectively improve the estimated accuracy and robustness of the association probabilities between the objects and the observations, and have the ability to track accurately multiple objects in the complex background and long-time occlusion environment.