A hierarchical model incorporating segmented regions and pixel descriptors for video background subtraction

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

83 Scopus Citations
View graph of relations

Author(s)

  • Shengyong Chen
  • Jianhua Zhang
  • Youfu Li
  • Jianwei Zhang

Detail(s)

Original languageEnglish
Article number6059503
Pages (from-to)118-127
Journal / PublicationIEEE Transactions on Industrial Informatics
Volume8
Issue number1
Publication statusPublished - Feb 2012

Abstract

Background subtraction is important for detecting moving objects in videos. Currently, there are many approaches to performing background subtraction. However, they usually neglect the fact that the background images consist of different objects whose conditions may change frequently. In this paper, a novel hierarchical background model is proposed based on segmented background images. It first segments the background images into several regions by the mean-shift algorithm. Then, a hierarchical model, which consists of the region models and pixel models, is created. The region model is a kind of approximate Gaussian mixture model extracted from the histogram of a specific region. The pixel model is based on the cooccurrence of image variations described by histograms of oriented gradients of pixels in each region. Benefiting from the background segmentation, the region models and pixel models corresponding to different regions can be set to different parameters. The pixel descriptors are calculated only from neighboring pixels belonging to the same object. The experimental results are carried out with a video database to demonstrate the effectiveness, which is applied to both static and dynamic scenes by comparing it with some well-known background subtraction methods. © 2006 IEEE.

Research Area(s)

  • Background subtraction, cooccurrence of image variation, hierarchical background model (HBM), pixel model, region segmentation

Citation Format(s)