Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2425836.2425931acmotherconferencesArticle/Chapter ViewAbstractPublication PagesivcnzConference Proceedingsconference-collections
poster

Foreground segmentation for interactive displays

Published: 26 November 2012 Publication History

Abstract

We describe a method for segmenting the foreground of a live video stream. We use two Gaussian mixture models, combining cues from color and depth images to provide a high fidelity foreground estimation. This estimation is then used to power a sandbox game on an interactive public display. Our method achieves an accuracy of 90% in a variety of conditions.

References

[1]
Y. Boykov and M. Jolly. Interactive graph cuts for optimal boundary & region segmentation of objects in nd images. In Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, volume 1, pages 105--112. IEEE, 2001.
[2]
Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 23(11): 1222--1239, 2001.
[3]
R. Crabb, C. Tracey, A. Puranik, and J. Davis. Real-time foreground segmentation via range and color imaging. In Computer Vision and Pattern Recognition Workshops, 2008. CVPRW'08. IEEE Computer Society Conference on, pages 1--5. IEEE, 2008.
[4]
A. Criminisi, G. Cross, A. Blake, and V. Kolmogorov. Bilayer segmentation of live video. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, volume 1, pages 53--60. IEEE, 2006.
[5]
A. Elgammal, D. Harwood, and L. Davis. Non-parametric model for background subtraction. Computer Vision - ECCV 2000, pages 751--767, 2000.
[6]
G. Gordon, T. Darrell, M. Harville, and J. Woodfill. Background estimation and removal based on range and color. In Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., volume 2. IEEE, 1999.
[7]
B. Han, D. Comaniciu, and L. Davis. Sequential kernel density approximation through mode propagation: applications to background modeling. In Proc. ACCV, volume 2004, 2004.
[8]
M. Harville, G. Gordon, and J. Woodfill. Foreground segmentation using adaptive mixture models in color and depth. In Detection and Recognition of Events in Video, 2001. Proceedings. IEEE Workshop on, pages 3--11. IEEE, 2001.
[9]
T. Horprasert, D. Harwood, and L. Davis. A statistical approach for real-time robust background subtraction and shadow detection. In IEEE ICCV, volume 99, pages 256--261. Citeseer, 1999.
[10]
O. Javed, K. Shafique, and M. Shah. A hierarchical approach to robust background subtraction using color and gradient information. In Motion and Video Computing, 2002. Proceedings. Workshop on, pages 22--27. IEEE, 2002.
[11]
B. Lo and S. Velastin. Automatic congestion detection system for underground platforms. In Intelligent Multimedia, Video and Speech Processing, 2001. Proceedings of 2001 International Symposium on, pages 158--161. IEEE, 2001.
[12]
N. Oliver, B. Rosario, and A. Pentland. A bayesian computer vision system for modeling human interactions. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(8): 831--843, 2000.
[13]
M. Piccardi. Background subtraction techniques: a review. In Systems, Man and Cybernetics, 2004 IEEE International Conference on, volume 4, pages 3099--3104. Ieee, 2004.
[14]
P. Power and J. Schoonees. Understanding background mixture models for foreground segmentation. In Proceedings Image and Vision Computing New Zealand, volume 2002, 2002.
[15]
C. Ridder, O. Munkelt, and H. Kirchner. Adaptive background estimation and foreground detection using kalman-filtering. In Proceedings of International Conference on recent Advances in Mechatronics, pages 193--199. Citeseer, 1995.
[16]
C. Rother, V. Kolmogorov, and A. Blake. Grabcut: Interactive foreground extraction using iterated graph cuts. In ACM Transactions on Graphics (TOG), volume 23, pages 309--314. ACM, 2004.
[17]
M. Seki, T. Wada, H. Fujiwara, and K. Sumi. Background subtraction based on cooccurrence of image variations. In Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, volume 2, pages II--65. IEEE, 2003.
[18]
C. Stauffer and W. Grimson. Adaptive background mixture models for real-time tracking. In Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., volume 2. IEEE, 1999.
[19]
L. Wang, C. Zhang, R. Yang, and C. Zhang. Tofcut: Towards robust real-time foreground extraction using a time-of-flight camera. In Proc. of 3DPVT, 2010.
[20]
C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland. Pfinder: Real-time tracking of the human body. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(7): 780--785, 1997.
[21]
T. Yu, C. Zhang, M. Cohen, Y. Rui, and Y. Wu. Monocular video foreground/background segmentation by tracking spatial-color gaussian mixture models. In Motion and Video Computing, 2007. WMVC'07. IEEE Workshop on, pages 5--5. IEEE, 2007.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
IVCNZ '12: Proceedings of the 27th Conference on Image and Vision Computing New Zealand
November 2012
547 pages
ISBN:9781450314732
DOI:10.1145/2425836
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • HRS: Hoare Research Software Ltd.
  • Google Inc.
  • Dept. of Information Science, Univ.of Otago: Department of Information Science, University of Otago, Dunedin, New Zealand

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 November 2012

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Poster

Conference

IVCNZ '12
Sponsor:
  • HRS
  • Dept. of Information Science, Univ.of Otago
IVCNZ '12: Image and Vision Computing New Zealand
November 26 - 28, 2012
Dunedin, New Zealand

Acceptance Rates

Overall Acceptance Rate 55 of 74 submissions, 74%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 96
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 25 Jan 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media