The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
Abstract
Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient. We propose an approach based on self organization through artificial neural networks, widely applied in human image processing systems and more generally in cognitive science. The proposed approach can handle scenes containing moving backgrounds, gradual illumination variations and camouflage, has no bootstrapping limitations, can include into the background model shadows cast by moving objects, and achieves robust detection for different types of videos taken with stationary cameras. We compare our method with other modeling techniques and report experimental results, both in terms of detection accuracy and in terms of processing speed, for color video sequences that represent typical situations critical for video surveillance systems.
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Cites
A model of saliency-based visual attention for rapid scene analysis
Itti, L., Koch, C., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,271
Self-Organization and Associative Memory
Kohonen, T. (1989) · Springer Series in Information Sciences · doi:10.1007/978-3-642-88163-3 · Cited by 8,740
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
Performance of optical flow techniques
Barron, J. L., Fleet, D. J., & Beauchemin, S. S. (1994) · International Journal of Computer Vision · doi:10.1007/bf01420984 · Cited by 4,305
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
Self-Organization and Associative Memory
Kohonen, T. (1988) · Springer Series in Information Sciences · doi:10.1007/978-3-662-00784-6 · Cited by 3,812
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
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
VisualSEEk: a fully automated content-based image query system
Smith, J. R., & Chang, S. (1996) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/244130.244151 · Cited by 1,745
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
Evaluation of background subtraction techniques for video surveillance
Brutzer, S., Hoferlin, B., & Heidemann, G. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995508 · Cited by 622
Background segmentation with feedback: The Pixel-Based Adaptive Segmenter
Hofmann, M., Tiefenbacher, P., & Rigoll, G. (2012) · 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2012.6238925 · Cited by 579
Bouwmans, 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
Metadata
- Authors
- Maddalena, L., & Petrosino, A.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2008-07
- Volume
- 17
- Issue
- 7
- Pages
- 1168-1177
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2141572457
- Cited by
- 786
- References
- 18
- FWCI
- 31.98
- Type
- journal article
- Language
- en
- Open access
- closed
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