A unified approach to background adaptation and initialization in public scenes

D. Park, H. Byun

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Foreground detection methods generally assume that backgrounds are observed more frequently than foregrounds are, but the assumption is not valid in public scenes. Viewing background adaptation in public scenes as a unified problem with background initialization and stationary object detection, we formulate it as an energy minimization problem in dynamic Markov random fields. Constraining the connections among the sites with spatiotemporal reliabilities, we robustly handle object-wise changes and efficiently minimize the energy terms with a coordinate descent method. Evaluated with realistic sequences from i-LIDS, PETS, ETISEO and changedetection.net datasets, the proposed method outperforms state-of-the-art methods and temporal parameter adjustment.

Original languageEnglish
Pages (from-to)1985-1997
Number of pages13
JournalPattern Recognition
Issue number7
Publication statusPublished - 2013 Jul

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) ( 2010-0013737 ). We appreciate the tireless efforts of Nil Goyette for Chagedetection.net dataset [36] .

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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