Learning a scene background model via classification

Horng Hong Lin*, Tyng Luh Liu, Jen-Hui Chuang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

59 Scopus citations


Learning to efficiently construct a scene background model is crucial for tracking techniques relying on background subtraction. Our proposed method is motivated by criteria leading to what a general and reasonable background model should be, and realized by a practical classification technique. Specifically, we consider a two-level approximation scheme that elegantly combines the bottom-up and top-down information for deriving a background model in real time. The key idea of our approach is simple but effective: If a classifier can be used to determine which image blocks are part of the background, its outcomes can help to carry out appropriate blockwise updates in learning such a model. The quality of the solution is further improved by global validations of the local updates to maintain the interblock consistency. And a complete background model can then be obtained based on a measurement of model completion. To demonstrate the effectiveness of our method, various experimental results and comparisons are included.

Original languageEnglish
Pages (from-to)1641-1654
Number of pages14
JournalIEEE Transactions on Signal Processing
Issue number5
StatePublished - 25 May 2009


  • Background modeling
  • Boosting
  • Classification
  • SVM
  • Tracking

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