Incremental nonparametric discriminant analysis for robust object tracking

In this paper, a new adaptive subspace learning model based on incremental nonparametric discriminant analysis (INDA) is proposed for visual tracking. Traditional subspace trackers focus on updating eigenvectors in handling with appearance variation of the target object, ignoring the non-target background region during tracking. The INDA features take both of them into consideration, thereby promoting the tracking process in the ever-changing environment. Meanwhile, INDA relaxes the Gaussian assumption in Fisher discriminant analysis (FDA), so it can handle more general class distributions problem. The scatter matrices are also reformulated to update the subspace incrementally based on previous results. In conjunction with efficient feature extraction method, the system is real time capable. Numerous experiments show the superiority of our tracker over current states of art methods on several publicly available datasets.