SWCD: a sliding window and self-regulated learning-based background updating method for change detection in videos

IŞIK Ş., ÖZKAN K., GÜNAL S., GEREK Ö. N.

JOURNAL OF ELECTRONIC IMAGING, cilt.27, sa.2, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 2
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1117/1.jei.27.2.023002
  • Dergi Adı: JOURNAL OF ELECTRONIC IMAGING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: foreground segmentation, sliding window approach, change detection
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

Change detection with background subtraction process remains to be an unresolved issue and attracts research interest due to challenges encountered on static and dynamic scenes. The key challenge is about how to update dynamically changing backgrounds from frames with an adaptive and self-regulated feedback mechanism. In order to achieve this, we present an effective change detection algorithm for pixel-wise changes. A sliding window approach combined with dynamic control of update parameters is introduced for updating background frames, which we called sliding window-based change detection. Comprehensive experiments on related test videos show that the integrated algorithm yields good objective and subjective performance by overcoming illumination variations, camera jitters, and intermittent object motions. It is argued that the obtained method makes a fair alternative in most types of foreground extraction scenarios; unlike case-specific methods, which normally fail for their nonconsidered scenarios. (c) 2018 SPIE and IS&T