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In this paper, a memory-based Gaussian Mixture Model (MGMM) is proposed inspired by the way human perceives the environment. The human memory mechanism is introduced to model the background, which can make the model remember what the scene has ever been and help the model adapt to the variation of the scene more quickly. Experimental results show the effect of the memory mechanism in segmenting moving...
Gaussian Mixture Model (GMM) is one of the best models for modeling a background scene with gradual changes and repetitive motions. However, it fails in segmenting moving objects when the scene changes sharply. To handle this problem, an improved background modeling algorithm-Intelligent GMM (IGMM), which is inspired by the way human perceive the environment to tackle sharp changes in the scene, is...
In this paper we present a segmentation system for monocular video sequences with static camera that aims at foreground/background separation and tracking. We propose to combine a simple pixel-wise model for the background with a general purpose region based model for the foreground. The background is modeled using one Gaussian per pixel, thus achieving a precise and easy to update model. The foreground...
We investigate effective means of building robust dictionaries for detecting the sparse foreground in videos with static background. This work is an extension to our existing solution to foreground/background segmentation problem using the linear programming method proposed to detect sparse errors in signals, which are created by a known dictionary. The dictionary building methods we study are established...
Adaptive Gaussian mixtures are widely used to model the dynamic background for real-time object detection. Recently the convergence speed of this approach is improved and a relatively robust statistical framework is proposed by Lee (PAMI, 2005). However, object quality still remains unacceptable due to poor Gaussian mixture quality, susceptibility to background/foreground data proportion, and inability...
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