Evaluation report of integrated background modeling based on spatio-temporal features

Yosuke Nonaka, Atsushi Shimada, Hajime Nagahara, Rin-Ichiro Taniguchi

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

37 Citations (Scopus)

Abstract

We report evaluation results of an integrated background modeling based on spatio-temporal features. The background modeling method consists of three complementary approaches: pixel-level background modeling, region-level one and frame-level one. The pixel-level background model uses the probability density function to approximate background model. The PDF is estimated non-parametrically by using Parzen density estimation. The region-level model is based on the evaluation of the local texture around each pixel while reducing the effects of variations in lighting. The frame-level model detects sudden, global changes of the the image brightness and estimates a present background image from input image referring to a background model image. Then, objects are extracted by background subtraction. Fusing these approaches realizes robust object detection under varying illumination.

Original languageEnglish
Title of host publication2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012
Pages9-14
Number of pages6
DOIs
Publication statusPublished - Aug 20 2012
Event2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012 - Providence, RI, United States
Duration: Jun 16 2012Jun 21 2012

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Other

Other2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012
CountryUnited States
CityProvidence, RI
Period6/16/126/21/12

Fingerprint

Pixels
Lighting
Probability density function
Luminance
Textures
Object detection

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Cite this

Nonaka, Y., Shimada, A., Nagahara, H., & Taniguchi, R-I. (2012). Evaluation report of integrated background modeling based on spatio-temporal features. In 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012 (pp. 9-14). [6238920] (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). https://doi.org/10.1109/CVPRW.2012.6238920

Evaluation report of integrated background modeling based on spatio-temporal features. / Nonaka, Yosuke; Shimada, Atsushi; Nagahara, Hajime; Taniguchi, Rin-Ichiro.

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012. 2012. p. 9-14 6238920 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops).

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

Nonaka, Y, Shimada, A, Nagahara, H & Taniguchi, R-I 2012, Evaluation report of integrated background modeling based on spatio-temporal features. in 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012., 6238920, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 9-14, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012, Providence, RI, United States, 6/16/12. https://doi.org/10.1109/CVPRW.2012.6238920
Nonaka Y, Shimada A, Nagahara H, Taniguchi R-I. Evaluation report of integrated background modeling based on spatio-temporal features. In 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012. 2012. p. 9-14. 6238920. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). https://doi.org/10.1109/CVPRW.2012.6238920
Nonaka, Yosuke ; Shimada, Atsushi ; Nagahara, Hajime ; Taniguchi, Rin-Ichiro. / Evaluation report of integrated background modeling based on spatio-temporal features. 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012. 2012. pp. 9-14 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops).
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