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.
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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
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A comparative study of texture measures with classification based on featured distributions
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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
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Wallflower: principles and practice of background maintenance
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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
Detecting 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 modeling and subtraction of dynamic scenes
Monnet, Mittal, Paragios, et al. (2003) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238641 · Cited by 401
Background modeling and subtraction by codebook construction
Kim, K., Chalidabhongse, T. H., Harwood, D., & Davis, L. S. (2005) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2004.1421759 · Cited by 382
Detection and location of people in video images using adaptive fusion of color and edge information
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Detecting salient motion by accumulating directionally-consistent flow
Wixson, L. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868680 · Cited by 240
A Texture-based Method for Detecting Moving Objects
Heikkilä, M., Pietikäinen, M., & Heikkilä, J. (2004) · British Machine Vision Conference (BMVC) · doi:10.5244/c.18.21 · Cited by 149
Using histograms to detect and track objects in color video
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Robust background subtraction and maintenance
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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
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-citation
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
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