article

A model for dynamic object segmentation with kernel density estimation based on gradient features

Authors:

Zheng Li,

Pohuang Jiang,

Hong Ma,

Jian Yang,

DongMing TangAuthors Info & Claims

Image and Vision Computing, Volume 27, Issue 6

Pages 817 - 823

https://doi.org/10.1016/j.imavis.2008.08.004

Published: 01 May 2009 Publication History

Abstract

The dynamic object segmentation in videos taken from a static camera is a basic technique in many vision surveillance applications. In order to suppress fake objects caused by dynamic cast shadows and reflection images, this paper presents a novel segmentation model with the function of cast shadow and reflection image suppression. This model is a kernel density estimation model based on dynamic gradient features. Unlike the conventional kernel density estimation model which can only suppress cast shadows in color videos, this model can also suppress them in intensity videos, and under the circumstance of diffusion it can suppress reflection images effectively. Although this model may cause the increase of the false negative rate, its function of fake object suppression is remarkable. Furthermore, the false negative rate can be reduced with other convenient methods. Some experimental results by real videos are also presented in this paper to demonstrate the effectiveness of this model.

References

[1]

A. Elgmmal, R. Duraiswami, D. Harwood, L.S. Davis, Background and foreground modeling using nonparametric kernel density estimation for visual surveillance, in: Proceeding of the IEEE, vol. 90, IEEE Institute of Electrical and Electronics, USA, July 2002, Issue 7, pp. 1151-1163.

Google Scholar

[2]

A. Mittal, N. Paragios, Motion-based background subtraction using adaptive kernel density estimation, in: CVPR'04, vol. 2, IEEE Computer Society, Washington, DC, USA, July 2004, pp. II302-II309.

Crossref

Google Scholar

[3]

A. Elgmmal, R. Duraiswami, L.S. Davis, Efficient non-parametric adaptive color modeling using fast gauss transform, in: CVPR'01, vol. 2, IEEE Computer Society, Kauai, HI, USA, 2001, pp. II563-II570.

Google Scholar

[4]

G. Economou, A. Fotinos, S. Makrogiannis, S. Fotopoulos, Color image edge detection based on nonparametric density estimation, in: ICIP'01, vol. 1, IEEE Signal Processing Society, Thessaloniki, Greece, 2001, pp. 922-925.

Google Scholar

[5]

J. Rittscher, J. Kato, S. Joga, A. Blake, A probabilistic background model for tracking, in: ECCV'00, Springer-Verlag, Dublin, Ireland, Part II, 2000, pp. 336-350.

Crossref

Google Scholar

[6]

C. Stauffer, W.E.L. Grimson, Adaptive background mixture models for realtime tracking, in: CVPR'99, vol. 2, IEEE Computer Society, Ft. Collins, CO, USA, 1999, pp. 246-252.

Google Scholar

[7]

W.E.L. Grimson, C. Stauffer, R. Romano, L. Lee, Using adaptive tracking to classify and monitor activities in a site, in: CVPR'98, Santa Barbara, CA, USA, June 1998, pp. 22-29.

Crossref

Google Scholar

[8]

N. Friedman, S. Russell, Image segmentation in video sequences: a probabilistic approach, in: UAI'97, Morgan Kaufmann Publishers, Providence, Rhode Island, USA, 1997, pp. 175-181.

Crossref

Google Scholar

[9]

A. Yoneyama, C.H. Yeh, C.-C.J. Kuo, Moving cast shadow elimination for robust vehicle extraction based on 2D joint vehicle/shadow models, in: AVSS'03, IEEE Computer Society, Miami, FL, USA, July 2003, pp. 229-236.

Crossref

Google Scholar

[10]

E. Salvador, A. Cavallaro, T. Ebrahimi, Cast shadow Segmentation using invariant color features, Computer Vision and Image Understanding, vol. 95, Elsevier Science Inc., New York, USA, August 2004, Issue 2, pp. 238-259.

Digital Library

Google Scholar

[11]

R. Souvenir, J. Wright, R. Pless, Spatio-temporal detection and isolation: results on PETS 2005 DataSets, in: PETS'05, Breckenridge, CO, USA, January 2005.

Google Scholar

[12]

M.A. El Saban, B.S. Manjunath, Video Region segmentation by spatio-temporal watersheds, in: ICIP'03, vol. 1, IEEE Computer Press, Barcelona, Spain, September 2003, pp. I-349-I-352.

Google Scholar

[13]

J. Migdal, W.E.L. Grimson, Background subtraction using markov thresholds, in: WACV/MOTION'05, vol. 2, IEEE Computer Society, Breckenridge, CO, USA, January, 2005, pp. 58-65.

Crossref

Google Scholar

[14]

D. Duque, H. Santos, P. Cortez, Moving object detection unaffected by cast shadows, highlights and ghosts, in: ICIP'05, vol. 3, September 2005, pp. III-413-III-416.

Google Scholar

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Escudero-Viñolo MBescos J(2020)Squeezing the DCT to Fight CamouflageJournal of Mathematical Imaging and Vision10.1007/s10851-019-00930-y62:2(206-222)Online publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1007/s10851-019-00930-y
Mandal ISairam N(2012)Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis SystemJournal of Medical Systems10.1007/s10916-012-9828-036:5(3353-3373)Online publication date: 1-Oct-2012
https://dl.acm.org/doi/10.1007/s10916-012-9828-0

A model for dynamic object segmentation with kernel density estimation based on gradient features
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

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Published In

Image and Vision Computing Volume 27, Issue 6

May, 2009

225 pages

ISSN:0262-8856

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Butterworth-Heinemann

United States

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Published: 01 May 2009

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Escudero-Viñolo MBescos J(2020)Squeezing the DCT to Fight CamouflageJournal of Mathematical Imaging and Vision10.1007/s10851-019-00930-y62:2(206-222)Online publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1007/s10851-019-00930-y
Mandal ISairam N(2012)Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis SystemJournal of Medical Systems10.1007/s10916-012-9828-036:5(3353-3373)Online publication date: 1-Oct-2012
https://dl.acm.org/doi/10.1007/s10916-012-9828-0

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Gradient-domain volumetric photon density estimation

Gradient-Domain Photon Density Estimation

Simple shadow removal using shadow depth map and illumination-invariant feature

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