English  |  正體中文  |  简体中文  |  Items with full text/Total items : 90453/105672 (86%)
Visitors : 13344346      Online Users : 390
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version

    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/101192

    Title: Robust Techniques for Abandoned and Removed Object Detection Based on Markov Random Field
    Authors: 林智揚;Chih-Yang Lin;*;Kahlil Muchtar;Chia-Hung Yeh
    Contributors: 生物資訊與醫學工程學系
    Date: 2016-08
    Issue Date: 2016-09-20 11:25:17 (UTC+8)
    Abstract: 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.
    Appears in Collections:[生物資訊與醫學工程學系 ] 期刊論文

    Files in This Item:

    File SizeFormat

    All items in ASIAIR are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback