Adaptive background modeling for paused object regions

Atsushi Shimad; Satoshi Yoshinaga; Rin Ichiro Taniguchi

doi:10.1007/978-3-642-22822-3_2

Adaptive background modeling for paused object regions

Atsushi Shimad, Satoshi Yoshinaga, Rin Ichiro Taniguchi

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers
Pages	12-22
Number of pages	11
Edition	PART1
DOIs	https://doi.org/10.1007/978-3-642-22822-3_2
Publication status	Published - Sep 28 2011
Event	International Workshops on Computer Vision, ACCV 2010 - Queenstown, New Zealand Duration: Nov 8 2010 → Nov 9 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART1
Volume	6468 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	International Workshops on Computer Vision, ACCV 2010
Country	New Zealand
City	Queenstown
Period	11/8/10 → 11/9/10

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-22822-3_2

Fingerprint Dive into the research topics of 'Adaptive background modeling for paused object regions'. Together they form a unique fingerprint.

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 proceeding › Conference 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).

@inproceedings{7393f171aaf548efb3489b5dd1f83327,

title = "Adaptive background modeling for paused object regions",

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.",

author = "Atsushi Shimad and Satoshi Yoshinaga and Taniguchi, {Rin Ichiro}",

year = "2011",

month = sep,

day = "28",

doi = "10.1007/978-3-642-22822-3_2",

language = "English",

isbn = "9783642228216",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART1",

pages = "12--22",

booktitle = "Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers",

edition = "PART1",

note = "International Workshops on Computer Vision, ACCV 2010 ; Conference date: 08-11-2010 Through 09-11-2010",

}

TY - GEN

T1 - Adaptive background modeling for paused object regions

AU - Shimad, Atsushi

AU - Yoshinaga, Satoshi

AU - Taniguchi, Rin Ichiro

PY - 2011/9/28

Y1 - 2011/9/28

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=80053125931&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053125931&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-22822-3_2

DO - 10.1007/978-3-642-22822-3_2

M3 - Conference contribution

AN - SCOPUS:80053125931

SN - 9783642228216

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 12

EP - 22

BT - Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers

T2 - International Workshops on Computer Vision, ACCV 2010

Y2 - 8 November 2010 through 9 November 2010

ER -