The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
A texture-based method for modeling the background and detecting moving objects
Abstract
This paper presents a novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence. Each pixel is modeled as a group of adaptive local binary pattern histograms that are calculated over a circular region around the pixel. The approach provides us with many advantages compared to the state-of-the-art. Experimental results clearly justify our model.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
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
A comparative study of texture measures with classification based on featured distributions
Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882
Self-citationPfinder: 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
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
Wallflower: principles and practice of background maintenance
Toyama, K., Krumm, J., Brumitt, B., & Meyers, B. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.791228 · Cited by 1,696
Improved Adaptive Gaussian Mixture Model for Background Subtraction
Živković, Z. (2004) · Cited by 1,671
Elgammal, A., Duraiswami, R., Harwood, D., & Davis, L. S. (2002) · Proceedings of the IEEE · doi:10.1109/jproc.2002.801448 · Cited by 1,508
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
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
Cited by
A survey of advances in vision-based human motion capture and analysis
Moeslund, T. B., Hilton, A., & Krüger, V. (2006) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.08.002 · Cited by 2,541
Local Binary Patterns and Its Application to Facial Image Analysis: A Survey
Huang, D., Shan, C., Ardabilian, M., Wang, Y., & Chen, L. (2011) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2118750 · Cited by 905
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
Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling
Karsch, K., Liu, C., & Kang, S. B. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2316835 · Cited by 453
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
Bouwmans, T., Javed, S., Sultana, M., & Jung, S. K. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.04.024 · Cited by 396
Local binary features for texture classification: Taxonomy and experimental study
Liu, L., Fieguth, P., Guo, Y., Wang, X., & Pietikäinen, M. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.08.032 · Cited by 369
Self-citationBackground 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
Deep background subtraction with scene-specific convolutional neural networks
Braham, M., & Van Droogenbroeck, M. (2016) · 2016 International Conference on Systems, Signals and Image Processing (IWSSIP) · doi:10.1109/iwssip.2016.7502717 · Cited by 313
Survey on LBP based texture descriptors for image classification
Nanni, L., Lumini, A., & Brahnam, S. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.09.054 · Cited by 260
Experimental and computational framework for a dynamic protein atlas of human cell division
Cai, Y., Hossain, M. J., Hériché, J.-K., Politi, A. Z., Walther, N., Koch, B., Wachsmuth, M., Nijmeijer, B., Kueblbeck, M., Martinic-Kavur, M., et al. (2018) · Nature · doi:10.1038/s41586-018-0518-z · Cited by 121
Attractive-and-repulsive center-symmetric local binary patterns for texture classification
El merabet, Y., Ruichek, Y., & El idrissi, A. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2018.11.011 · Cited by 86
Clustering method for counting passengers getting in a bus with single camera
Yang, T., Zhang, Y., Shao, D., & Li, Y. (2010) · Optical Engineering · doi:10.1117/1.3374439 · Cited by 27
Kandylakis, Z., Vasili, K., & Karantzalos, K. (2019) · Remote Sensing · doi:10.3390/rs11040446 · Cited by 15
Metadata
- Authors
- Heikkila, M., & Pietikainen, M.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2006-04
- Volume
- 28
- Issue
- 4
- Pages
- 657-662
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2098305432
- Cited by
- 1,249
- References
- 18
- FWCI
- 49.79
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.
