Modeling of Dynamic Backgrounds by Type-2 Fuzzy Gaussians Mixture Models

Thierry Bouwmans (1) , Fida El Baf (1)

1 MIA - Mathématiques, Image et Applications - EA 3165

Gaussian Mixture Models (GMMs) are the most popular techniques in background modeling but present some limitations when some dynamic changes occur like camera jitter, illumination changes, movement in the background. Furthermore, the GMM are 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. In this context, we propose to model the background by using a Type-2 Fuzzy Gaussian Mixture Models. The interest is to introduce descriptions of uncertain parameters in the GMM. Experimental validation of the proposed method is performed and presented on a diverse set of RGB and infrared videos. Results show the relevance of the proposed approach.

Mots clés: Background Modeling; Foreground Detection; Mixture of Gaussians

Domaines: Traitement des images [eess.IV]

Thierry Bouwmans, Fida El Baf. Modeling of Dynamic Backgrounds by Type-2 Fuzzy Gaussians Mixture Models. MASAUM Journal of of Basic and Applied Sciences, 2010, 1 (2), pp.265-276. ⟨hal-00452820⟩