Robust techniques for abandoned and removed object detection based on Markov random field

Chih Yang Lin, Kahlil Muchtar, Chia Hung Yeh*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


This paper presents a novel framework for detecting abandoned objects by introducing a fully-automatic GrabCut object segmentation. GrabCut seed initialization is treated as a background (BG) modelling problem that focuses only on unhanded objects and objects that become immobile. The BG distribution is constructed with dual Gaussian mixtures that are comprised of high and low learning rate models. We propose a primitive BG model-based removed object validation and Haar feature-based cascade classifier for still-people detection once a candidate for a released object has been detected. Our system can obtain more robust and accurate results for real environments based on evaluations of realistic scenes from CAVIAR, PETS2006, CDnet 2014, and our own datasets.

Original languageEnglish
Pages (from-to)181-195
Number of pages15
JournalJournal of Visual Communication and Image Representation
Publication statusPublished - 2016 Aug 1
Externally publishedYes


  • Abandoned object detection
  • Background modelling
  • GMM
  • Markov random field

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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