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Exemplar-based background model initialization

Published: 11 November 2005 Publication History

Abstract

Most of the automated video-surveillance applications are based on background (BG) subtraction techniques, that aim at distinguishing moving objects in a static scene. These strategies strongly depend on the BG model, that has to be initialized and updated. A good initialization is crucial for the successive processing. In this paper, we propose a novel method for BG initialization and recovery, that merges interesting ideas coming from the video inpainting and the generative modelling subfields. The method takes as input a video sequence, in which several objects move in front of a stationary BG. Then, a statistical representation of the BG is iteratively built, discarding automatically the moving objects. The method is based on the following hypotheses: (i) a portion of the BG, called sure BG, can be identified with high certainty by using only per-pixel reasoning and (ii) the remaining scene BG can be generated utilizing exemplars of the sure BG. The proposed algorithm is able to exploit these hypotheses in a principled and effective way.

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  • (2022)Fast and Accurate Background Reconstruction Using Background BootstrappingJournal of Imaging10.3390/jimaging80100098:1(9)Online publication date: 11-Jan-2022
  • (2022)ISAIR: Deep inpainted semantic aware image representation for background subtractionExpert Systems with Applications10.1016/j.eswa.2022.117947207(117947)Online publication date: Nov-2022
  • (2019)A Comprehensive Survey of Video Datasets for Background SubtractionIEEE Access10.1109/ACCESS.2019.29149617(59143-59171)Online publication date: 2019
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cover image ACM Conferences
VSSN '05: Proceedings of the third ACM international workshop on Video surveillance & sensor networks
November 2005
168 pages
ISBN:1595932429
DOI:10.1145/1099396
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 11 November 2005

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Author Tags

  1. background initialization
  2. background modelling
  3. video analysis
  4. video inpainting
  5. video surveillance

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MM&Sec '05
MM&Sec '05: Multimedia and Security Workshop 2005
November 11, 2005
Hilton, Singapore

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Cited By

View all
  • (2022)Fast and Accurate Background Reconstruction Using Background BootstrappingJournal of Imaging10.3390/jimaging80100098:1(9)Online publication date: 11-Jan-2022
  • (2022)ISAIR: Deep inpainted semantic aware image representation for background subtractionExpert Systems with Applications10.1016/j.eswa.2022.117947207(117947)Online publication date: Nov-2022
  • (2019)A Comprehensive Survey of Video Datasets for Background SubtractionIEEE Access10.1109/ACCESS.2019.29149617(59143-59171)Online publication date: 2019
  • (2019)Unsupervised deep context prediction for background estimation and foreground segmentationMachine Vision and Applications10.1007/s00138-018-0993-030:3(375-395)Online publication date: 17-May-2019
  • (2017)Extensive Benchmark and Survey of Modeling Methods for Scene Background InitializationIEEE Transactions on Image Processing10.1109/TIP.2017.272818126:11(5244-5256)Online publication date: Nov-2017
  • (2016)Performance Analysis of Background Estimation Methods for Video Surveillance ApplicationsProceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC – 16’)10.1007/978-3-319-30348-2_20(249-261)Online publication date: 23-Feb-2016
  • (2014)Background Model Initialization for Static CamerasBackground Modeling and Foreground Detection for Video Surveillance10.1201/b17223-5(3-1-3-16)Online publication date: 17-Jul-2014
  • (2011)A background subtraction algorithm for detecting and tracking vehiclesExpert Systems with Applications10.1016/j.eswa.2010.07.08338:3(1619-1631)Online publication date: Mar-2011
  • (2010)Background estimation using graph cuts and inpaintingProceedings of Graphics Interface 201010.5555/1839214.1839232(97-103)Online publication date: 31-May-2010
  • (2010)Background subtraction for automated multisensor surveillanceEURASIP Journal on Advances in Signal Processing10.1155/2010/3430572010(1-24)Online publication date: 1-Feb-2010
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