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

Chih Yang Lin, Kahlil Muchtar, Chia Hung Yeh*

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

10 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)181-195
頁數15
期刊Journal of Visual Communication and Image Representation
39
DOIs
出版狀態已發佈 - 2016 8月 1
對外發佈

ASJC Scopus subject areas

  • 訊號處理
  • 媒體技術
  • 電腦視覺和模式識別
  • 電氣與電子工程

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