Background Subtraction Using Infinite Asymmetric Gaussian Mixture Models With Simultaneous Feature Selection

Mixture models are broadly applied in image processing domains. Related existing challenges include failure to approximate exact data shapes, estimate correct number of components, and ignore irrelevant features. In this study, the authors develop a statistical self-refinement framework for the background subtraction task by using Dirichlet Process-based asymmetric Gaussian mixture model. The parameters of this model are learned using variational inference methods. They also incorporate feature selection simultaneously within the framework to avoid noisy influence from uninformative features. To validate the proposed framework, they report their results on background subtraction tasks on 8 different datasets for infrared and visible videos.