Non-parametric background and shadow modeling for object detection

Tatsuya Tanaka; Atsushi Shimada; Daisaku Arita; Rin Ichiro Taniguchi

doi:10.1007/978-3-540-76386-4_14

Non-parametric background and shadow modeling for object detection

Tatsuya Tanaka, Atsushi Shimada, Daisaku Arita, Rin Ichiro Taniguchi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

23 Citations (Scopus)

Abstract

We propose a fast algorithm to estimate background models using Parzen density estimation in non-stationary scenes. Each pixel has a probability density which approximates pixel values observed in a video sequence. It is important to estimate a probability density function fast and accurately. In our approach, the probability density function is partially updated within the range of the window function based on the observed pixel value. The model adapts quickly to changes in the scene and foreground objects can be robustly detected. In addition, applying our approach to cast-shadow modeling, we can detect moving cast shadows. Several experiments show the effectiveness of our approach.

Original language	English
Title of host publication	Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings
Publisher	Springer Verlag
Pages	159-168
Number of pages	10
Edition	PART 1
ISBN (Print)	9783540763857
DOIs	https://doi.org/10.1007/978-3-540-76386-4_14
Publication status	Published - 2007
Event	8th Asian Conference on Computer Vision, ACCV 2007 - Tokyo, Japan Duration: Nov 18 2007 → Nov 22 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	4843 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	8th Asian Conference on Computer Vision, ACCV 2007
Country/Territory	Japan
City	Tokyo
Period	11/18/07 → 11/22/07

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-76386-4_14

Cite this

Tanaka, T., Shimada, A., Arita, D., & Taniguchi, R. I. (2007). Non-parametric background and shadow modeling for object detection. In Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings (PART 1 ed., pp. 159-168). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4843 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-540-76386-4_14

Non-parametric background and shadow modeling for object detection. / Tanaka, Tatsuya; Shimada, Atsushi; Arita, Daisaku et al.
Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings. PART 1. ed. Springer Verlag, 2007. p. 159-168 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4843 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tanaka, T, Shimada, A, Arita, D & Taniguchi, RI 2007, Non-parametric background and shadow modeling for object detection. in Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 4843 LNCS, Springer Verlag, pp. 159-168, 8th Asian Conference on Computer Vision, ACCV 2007, Tokyo, Japan, 11/18/07. https://doi.org/10.1007/978-3-540-76386-4_14

Tanaka T, Shimada A, Arita D, Taniguchi RI. Non-parametric background and shadow modeling for object detection. In Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings. PART 1 ed. Springer Verlag. 2007. p. 159-168. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-540-76386-4_14

Tanaka, Tatsuya ; Shimada, Atsushi ; Arita, Daisaku et al. / Non-parametric background and shadow modeling for object detection. Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings. PART 1. ed. Springer Verlag, 2007. pp. 159-168 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

@inproceedings{d1b9a414dd77444a969c5ecf7ecc2d1f,

title = "Non-parametric background and shadow modeling for object detection",

abstract = "We propose a fast algorithm to estimate background models using Parzen density estimation in non-stationary scenes. Each pixel has a probability density which approximates pixel values observed in a video sequence. It is important to estimate a probability density function fast and accurately. In our approach, the probability density function is partially updated within the range of the window function based on the observed pixel value. The model adapts quickly to changes in the scene and foreground objects can be robustly detected. In addition, applying our approach to cast-shadow modeling, we can detect moving cast shadows. Several experiments show the effectiveness of our approach.",

author = "Tatsuya Tanaka and Atsushi Shimada and Daisaku Arita and Taniguchi, {Rin Ichiro}",

year = "2007",

doi = "10.1007/978-3-540-76386-4_14",

language = "English",

isbn = "9783540763857",

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

publisher = "Springer Verlag",

number = "PART 1",

pages = "159--168",

booktitle = "Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings",

address = "Germany",

edition = "PART 1",

note = "8th Asian Conference on Computer Vision, ACCV 2007 ; Conference date: 18-11-2007 Through 22-11-2007",

}

TY - GEN

T1 - Non-parametric background and shadow modeling for object detection

AU - Tanaka, Tatsuya

AU - Shimada, Atsushi

AU - Arita, Daisaku

AU - Taniguchi, Rin Ichiro

PY - 2007

Y1 - 2007

N2 - We propose a fast algorithm to estimate background models using Parzen density estimation in non-stationary scenes. Each pixel has a probability density which approximates pixel values observed in a video sequence. It is important to estimate a probability density function fast and accurately. In our approach, the probability density function is partially updated within the range of the window function based on the observed pixel value. The model adapts quickly to changes in the scene and foreground objects can be robustly detected. In addition, applying our approach to cast-shadow modeling, we can detect moving cast shadows. Several experiments show the effectiveness of our approach.

AB - We propose a fast algorithm to estimate background models using Parzen density estimation in non-stationary scenes. Each pixel has a probability density which approximates pixel values observed in a video sequence. It is important to estimate a probability density function fast and accurately. In our approach, the probability density function is partially updated within the range of the window function based on the observed pixel value. The model adapts quickly to changes in the scene and foreground objects can be robustly detected. In addition, applying our approach to cast-shadow modeling, we can detect moving cast shadows. Several experiments show the effectiveness of our approach.

UR - http://www.scopus.com/inward/record.url?scp=38149077423&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=38149077423&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-76386-4_14

DO - 10.1007/978-3-540-76386-4_14

M3 - Conference contribution

AN - SCOPUS:38149077423

SN - 9783540763857

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

SP - 159

EP - 168

BT - Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings

PB - Springer Verlag

T2 - 8th Asian Conference on Computer Vision, ACCV 2007

Y2 - 18 November 2007 through 22 November 2007

ER -

Non-parametric background and shadow modeling for object detection

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this