摘要： •A novel framework for detecting abandoned objects with automatic GrabCut is presented.•The Background (BG) distribution is constructed with dual Gaussian mixtures.•Our system can obtain more robust results for CAVIAR, PETS2006 & CDnet 2014 datasets. 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. 出版者： Elsevier Inc 出版日期： 2016-08 出處： Journal of visual communication and image representation, 2016-08, Vol.39, p.181-195 資源來源： Elsevier ScienceDirect Journals Complete 版權： 2016 Elsevier Inc. 識別號： ISSN: 1047-3203 識別號： EISSN: 1095-9076 識別號： DOI: 10.1016/j.jvcir.2016.05.024
