The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Non-parametric Model for Background Subtraction
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Citations
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
Pfinder: real-time tracking of the human body
Wren, C. R., Azarbayejani, A., Darrell, T., & Pentland, A. P. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598236 · Cited by 4,169
Multivariate Density Estimation: Theory, Practice, and Visualization
Scott, D. W. (1992) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316849 · Cited by 3,696
Image Segmentation in Video Sequences: A Probabilistic Approach
Friedman, N., & Russell, S. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.1539 · Cited by 833
Computer Image Processing and Recognition
(1981) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1981.4767058 · Cited by 542
Using adaptive tracking to classify and monitor activities in a site
Grimson, W. E. L., Stauffer, C., Romano, R., & Lee, L. (2002) · Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231) · doi:10.1109/cvpr.1998.698583 · Cited by 504
(1985) · Computer Vision, Graphics, and Image Processing · doi:10.1016/s0734-189x(85)80079-7 · Cited by 423
Cited by
Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560
W/sup 4/: real-time surveillance of people and their activities
Haritaoglu, I., Harwood, D., & Davis, L. S. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868683 · Cited by 2,523
Self-citationDetecting moving shadows: algorithms and evaluation
Prati, A., Mikic, I., Trivedi, M. M., & Cucchiara, R. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1206520 · Cited by 760
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
An Effective Method of Weld Defect Detection and Classification Based on Machine Vision
Sun, J., Li, C., Wu, X.-J., Palade, V., & Fang, W. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2896357 · Cited by 253
Metadata
- Authors
- Elgammal, A., Harwood, D., & Davis, L.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2000
- Pages
- 751-767
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1499877760
- Cited by
- 2,118
- References
- 7
- FWCI
- 32.07
- Type
- conference paper
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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How to cite
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- CoChat. (2026). Citation check: Non-parametric Model for Background Subtraction. https://app.cochat.ai/papers/10.1007/3-540-45053-x_48
- The paper
- Elgammal, A., Harwood, D., & Davis, L. (2000). Non-parametric Model for Background Subtraction. Lecture Notes in Computer Science. https://doi.org/10.1007/3-540-45053-x_48
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
