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Stochastic approximation for background modelling

2011; Elsevier BV; Volume: 115; Issue: 6 Linguagem: Inglês

10.1016/j.cviu.2011.01.007

1090-235X

Ezequiel López‐Rubio, Rafael Marcos Luque‐Baena,

Human Pose and Action Recognition

Many background modelling approaches are based on mixtures of multivariate Gaussians with diagonal covariance matrices. This often yields good results, but complex backgrounds are not adequately captured, and post-processing techniques are needed. Here we propose the use of mixtures of uniform distributions and multivariate Gaussians with full covariance matrices. These mixtures are able to cope with both dynamic backgrounds and complex patterns of foreground objects. A learning algorithm is derived from the stochastic approximation framework, which has a very reduced computational complexity. Hence, it is suited for real time applications. Experimental results show that our approach outperforms the classic procedure in several benchmark videos.