Adaptive background modeling for paused object regions

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Background modeling has been widely researched to detect moving objects from image sequences. Most approaches have a falsenegative problem caused by a stopped object. When a moving object stops in an observing scene, it will be gradually trained as background since the observed pixel value is directly used for updating the background model. In this paper, we propose 1) a method to inhibit background training, and 2) a method to update an original background region occluded by stopped object. We have used probabilistic approach and predictive approach of background model to solve these problems. The great contribution of this paper is that we can keep paused objects from being trained.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers
Pages12-22
Number of pages11
EditionPART1
DOIs
Publication statusPublished - Sep 28 2011
EventInternational Workshops on Computer Vision, ACCV 2010 - Queenstown, New Zealand
Duration: Nov 8 2010Nov 9 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART1
Volume6468 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshops on Computer Vision, ACCV 2010
CountryNew Zealand
CityQueenstown
Period11/8/1011/9/10

Fingerprint

Background Modeling
Moving Objects
Pixels
Probabilistic Approach
Image Sequence
Updating
Pixel
Update
Object
Background
Model

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Shimad, A., Yoshinaga, S., & Taniguchi, R. I. (2011). Adaptive background modeling for paused object regions. In Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers (PART1 ed., pp. 12-22). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6468 LNCS, No. PART1). https://doi.org/10.1007/978-3-642-22822-3_2

Adaptive background modeling for paused object regions. / Shimad, Atsushi; Yoshinaga, Satoshi; Taniguchi, Rin Ichiro.

Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers. PART1. ed. 2011. p. 12-22 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6468 LNCS, No. PART1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Shimad, A, Yoshinaga, S & Taniguchi, RI 2011, Adaptive background modeling for paused object regions. in Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers. PART1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART1, vol. 6468 LNCS, pp. 12-22, International Workshops on Computer Vision, ACCV 2010, Queenstown, New Zealand, 11/8/10. https://doi.org/10.1007/978-3-642-22822-3_2
Shimad A, Yoshinaga S, Taniguchi RI. Adaptive background modeling for paused object regions. In Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers. PART1 ed. 2011. p. 12-22. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART1). https://doi.org/10.1007/978-3-642-22822-3_2
Shimad, Atsushi ; Yoshinaga, Satoshi ; Taniguchi, Rin Ichiro. / Adaptive background modeling for paused object regions. Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers. PART1. ed. 2011. pp. 12-22 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART1).
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