Fida El Baf, Thierry Bouwmans, Bertrand Vachon. Type-2 Fuzzy Mixture of Gaussians Model: Application to Background Modeling. In George Bebis, Richard D. Boyle, Bahram Parvin, Darko Koracin, Paolo Remagnino, Fatih Murat Porikli, Jörg Peters, James T. Klosowski, Laura L. Arns, Yu Ka Chun, Theresa-Marie Rhyne, Laura Monroe, editors, Advances in Visual Computing, 4th International Symposium, ISVC 2008, Las Vegas, NV, USA, December 1-3, 2008. Proceedings, Part I. Volume 5358 of Lecture Notes in Computer Science, pages 772-781, Springer, 2008. [doi]
Background modeling is a key step of background subtraction methods used in the context of static camera. The goal is to obtain a clean background and then detect moving objects by comparing it with the current frame. Mixture of Gaussians Model [1] is the most popular technique and presents some limitations when dynamic changes occur in the scene like camera jitter, illumination changes and movement in the background. Furthermore, the MGM is initialized using a training sequence which may be noisy and/or insufficient to model correctly the background. All these critical situations generate false classification in the foreground detection mask due to the related uncertainty. To take into account this uncertainty, we propose to use a Type-2 Fuzzy Mixture of Gaussians Model. Results show the relevance of the proposed approach in presence of camera jitter, waving trees and water rippling.