Robust foreground detection in video using pixel layers

Kedar Patwardhan, Guillermo Sapiro, Vassilios Morellas

Research output: Contribution to journalArticle

80 Scopus citations

Abstract

A framework for robust foreground detection that works under difficult conditions such as dynamic background and moderately moving camera is presented in this paper. The proposed method includes two main components: coarse scene representation as the union of pixel layers, and foreground detection in video by propagating these layers using a maximum-likelihood assignment. We first cluster into "layers" those pixels that share similar statistics. The entire scene is then modeled as the union of such non-parametric layer-models. An in-coming pixel is detected as foreground if it does not adhere to these adaptive models of the background. A principled way of computing thresholds is used to achieve robust detection performance with a pre-specified number of false alarms. Correlation between pixels in the spatial vicinity is exploited to deal with camera motion without precise registration or optical flow. The proposed technique adapts to changes in the scene, and allows to automatically convert persistent foreground objects to background and re-convert them to foreground when they become {interesting}. This simple framework addresses the important problem of robust foreground and unusual region detection, at about 10 frames per second on a standard laptop computer. The presentation of the proposed approach is complemented by results on challenging real data and comparisons with other standard techniques.

Original languageEnglish (US)
Pages (from-to)746-751
Number of pages6
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume30
Issue number4
DOIs
StatePublished - Apr 1 2008

Keywords

  • Background subtraction
  • Foreground detection
  • Layer tracking
  • Scene analysis
  • Surveillance
  • Video analysis

Fingerprint Dive into the research topics of 'Robust foreground detection in video using pixel layers'. Together they form a unique fingerprint.

  • Cite this