TR2005-033

A Bayesian Approach to Background Modeling


Abstract:

Learning background statistics is an essential task for several visual surveillance applications such as incident detection and traffic management. In this paper, we propose a new method for modeling background statistics of dynamic scene. Each pixel is represented with layers of Gaussian distributions. Using recursive Bayesian learning, we estimate the probability distribution of mean and covariance of each Gaussian. The proposed algorithm preserves the multimodality of the background and estimates the number of necessary layers for representing each pixel. We compare our results with the Gaussian mixture background model. Experiments conducted on synthetic and video data demonstrate the superior performance of the proposed approach.

 

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    •  NEWS    MVIV 2005: publication by Oncel Tuzel and others
      Date: June 20, 2005
      Where: IEEE Workshop on Machine Vision for Intelligent Vehicles (MVIV)
      Research Area: Machine Learning
      Brief
      • The paper "A Bayesian Approach to Background Modeling" by Tuzel, O., Porikli, F. and Meer, P. was presented at the IEEE Workshop on Machine Vision for Intelligent Vehicles (MVIV).
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