Moving object segmentation in surveillance video based on adaptive mixtures

Research output: Contribution to conferencePaper

Abstract

This paper presents an adaptive mixtures-based method for segmenting moving objects in surveillance video with pixel-wise accuracy. The proposed method employs a Gaussian mixture model (GMM) to represent the intensity change of a pixel over time. The GMMconsists of a background component and one or more moving object component(s). The parameters of the GMM are estimated by using an adaptive algorithm that is a non-parametric and data-driven approach. The components in the GMM are subsequently classified into a background and moving objects according to their weights in the GMM. Experimental results demonstrate that the proposed method can successfully and robustly segment the moving objects in surveillance video.

Original languageEnglish
Pages1322-1325
Number of pages4
Publication statusPublished - 2013 Jan 1
Event2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013 - Nagoya, Japan
Duration: 2013 Sep 142013 Sep 17

Other

Other2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013
CountryJapan
CityNagoya
Period13/9/1413/9/17

Keywords

  • Adaptive mixture
  • Gaussian mixture model (GMM)
  • Moving object segmentation
  • Surveillance video

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • Cite this

    Zhang, X., Homma, N., Ichiji, K., Abe, M., Sugita, N., & Yoshizawa, M. (2013). Moving object segmentation in surveillance video based on adaptive mixtures. 1322-1325. Paper presented at 2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013, Nagoya, Japan.