The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Background segmentation with feedback: The Pixel-Based Adaptive Segmenter
Open accessAbstract
In this paper we present a novel method for foreground segmentation. Our proposed approach follows a non-parametric background modeling paradigm, thus the background is modeled by a history of recently observed pixel values. The foreground decision depends on a decision threshold. The background update is based on a learning parameter. We extend both of these parameters to dynamic per-pixel state variables and introduce dynamic controllers for each of them. Furthermore, both controllers are steered by an estimate of the background dynamics. In our experiments, the proposed Pixel-Based Adaptive Segmenter (PBAS) outperforms most state-of-the-art methods.
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Cites
Adaptive background mixture models for real-time tracking
Stauffer, C., & Grimson, W. E. L. (2003) · Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) · doi:10.1109/cvpr.1999.784637 · Cited by 6,923
Non-parametric Model for Background Subtraction
Elgammal, A., Harwood, D., & Davis, L. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45053-x_48 · Cited by 2,118
Background subtraction techniques: a review
Piccardi, M. (2005) · 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583) · doi:10.1109/icsmc.2004.1400815 · Cited by 2,083
Improved adaptive Gaussian mixture model for background subtraction
Zivkovic, Z. (2004) · Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. · doi:10.1109/icpr.2004.1333992 · Cited by 1,951
ViBe: A Universal Background Subtraction Algorithm for Video Sequences
Barnich, O., & Van Droogenbroeck, M. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2101613 · Cited by 1,885
Image change detection algorithms: a systematic survey
Radke, R. J., Andra, S., Al-Kofahi, O., & Roysam, B. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.838698 · Cited by 1,869
Improved Adaptive Gaussian Mixture Model for Background Subtraction
Živković, Z. (2004) · Cited by 1,671
An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection
KaewTraKulPong, P., & Bowden, R. (2002) · Video-Based Surveillance Systems · doi:10.1007/978-1-4615-0913-4_11 · Cited by 1,471
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
Maddalena, L., & Petrosino, A. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.924285 · Cited by 786
A consensus-based method for tracking: Modelling background scenario and foreground appearance
Wang, H., & Suter, D. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2006.05.024 · Cited by 163
Cited by
Traditional and recent approaches in background modeling for foreground detection: An overview
Bouwmans, T. (2014) · Computer Science Review · doi:10.1016/j.cosrev.2014.04.001 · Cited by 676
A deep convolutional neural network for video sequence background subtraction
Babaee, M., Dinh, D. T., & Rigoll, G. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.09.040 · Cited by 406
Self-citationBouwmans, T., Javed, S., Sultana, M., & Jung, S. K. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.04.024 · Cited by 396
Background subtraction in real applications: Challenges, current models and future directions
Garcia-Garcia, B., Bouwmans, T., & Rosales Silva, A. J. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2019.100204 · Cited by 337
Ammar, S., Bouwmans, T., Zaghden, N., & Neji, M. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.0769 · Cited by 43
Metadata
- Authors
- Hofmann, M., Tiefenbacher, P., & Rigoll, G.
- Venue
- 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 38-43
- ISSN
- 2160-7508 (ISSN-L)
- OpenAlex
- W1969977005
- Cited by
- 579
- References
- 10
- FWCI
- 30.09
- Type
- conference paper
- Language
- en
- Open access
- green
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- Hofmann, M., Tiefenbacher, P., & Rigoll, G. (2012). Background segmentation with feedback: The Pixel-Based Adaptive Segmenter. 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. https://doi.org/10.1109/cvprw.2012.6238925
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