Motion-based background subtraction using adaptive kernel density estimation

  • Authors:
  • Anurag Mittal;Nikos Paragios

  • Affiliations:
  • Siemens Corporate Research, Princeton, NJ;C.E.R.T.I.S., Ecole Nationale de Ponts et Chaussees, France

  • Venue:
  • CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
  • Year:
  • 2004

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Abstract

Background modeling is an important component of many vision systems. Existing work in the area has mostly addressed scenes that consist of static or quasi-static structures. When the scene exhibits a persistent dynamic behavior in time, such an assumption is violated and detection performance deteriorates. In this paper, we propose a new method for the modeling and subtraction of such scenes. Towards the modeling of the dynamic characteristics, optical flow is computed and utilized as a feature in a higher dimensional space. Inherent ambiguities in the computation of features are addressed by using a data-dependent bandwidth for density estimation using kernels. Extensive experiments demonstrate the utility and performance of the proposed approach.