Background subtraction: type-2 Fuzzy based methods

Saturday, 17 August 2013 00:29 Stefano Tommesani
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Type-2 Fuzzy GMM-UM of Baf et al (2008) paper link

 

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 classific ation in the foreground detection mask due to the related uncertainty. To take into ac count 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

 

Type-2 Fuzzy GMM-UV of Baf et al (2008) paper link

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 classific ation in the foreground detection mask due to the related uncertainty. To take into ac count 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

 

Type-2 Fuzzy GMM-UM with MRF of Zhao et al (2012) paper link1 paper link2

 

Based on Type-2 Fuzzy Gaussian Mixture Model (T2-FGMM) and Markov Random Field (MRF), we propose a novel background modeling method for motion detection in dynamic scenes. The key idea of the proposed approach is the successful introduction of the spatial-temporal constraints into the T2-FGMM by a Bayesian framework. The evaluation results in pixel level demonstrate that the proposed method performs better than the sound Gaussian Mixture Model (GMM) and T2-FGMM in such typical dynamic backgrounds as waving trees and water rippling

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Type-2 Fuzzy GMM-UV with MRF of Zhao et al (2012) paper link1 paper link2

Based on Type-2 Fuzzy Gaussian Mixture Model (T2-FGMM) and Markov Random Field (MRF), we propose a novel background modeling method for motion detection in dynamic scenes. The key idea of the proposed approach is the successful introduction of the spatial-temporal constraints into the T2-FGMM by a Bayesian framework. The evaluation results in pixel level demonstrate that the proposed method performs better than the sound Gaussian Mixture Model (GMM) and T2-FGMM in such typical dynamic backgrounds as waving trees and water rippling.


These algorithms are contained in the bgslibrary by Andrews Sobral, that includes over 30 background subtraction algorithms, a common C++ framework for comparing them, and an handy C++/MFC or Java app to see them running on video files or live feed from a webcam.

Return to the list of background subtraction algorithms

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Last Updated on Monday, 23 September 2013 17:33