The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Low-Rank Representation with Contextual Regularization for Moving Object Detection in Big Surveillance Video Data
Abstract
Modern video surveillance benefits greatly from advanced wireless imaging sensors and cloud data storage, thus, a vast amount of data is generated every second. Surveillance videos have thus become one of the biggest sources of unstructured data. Because a vast amount of surveillance videos is continuously and quickly produced at multiple locations, moving object detection in such a vast amount of these videos by using traditional detection methods is a challenging task. This paper presents a novel model that detects moving objects from such data sets based on low-rank representation with contextual regularization. Quantitative and qualitative assessments indicated that the proposed model significantly outperformed existing state-of-the-art moving object detection methods.
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“As a result, the foreground mask can be precisely identified with a 96% accuracy rate in 46 sequences.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [96]
Citations
Cites
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Barnich, O., & Droogenbroeck, M. V. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2101613 · Cited by 1,893
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Efficient Image Dehazing with Boundary Constraint and Contextual Regularization
Meng, G., Wang, Y., Duan, J., Xiang, S., & Pan, C. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.82 · Cited by 1,164
CDnet 2014: An Expanded Change Detection Benchmark Dataset
Wang, Y., Jodoin, P., Porikli, F., Konrad, J., Benezeth, Y., & Ishwar, P. (2014) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2014.126 · Cited by 883
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 791
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
MazumderRahul, HastieTrevor, & TibshiraniRobert (2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1859931 · Cited by 671
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation
Zhou, X., Yang, C., & Yu, W. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.132 · Cited by 639
Independent Component Analysis-Based Background Subtraction for Indoor Surveillance
Tsai, D., & Lai, S.-C. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.2007558 · Cited by 279
A Deep Semi-NMF Model for Learning Hidden Representations
Trigeorgis, G., Bousmalis, K., Zafeiriou, S. P., & Schuller, B. W. (2014) · Cited by 169
Research works printed but not linked: 1
- molisch, 2011, Video Coding, Wireless Communications, 565
Cited by
Sorted by citations
A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134
“Chen et al. [96] presented a model that used low-rank representation with contextual regularization to track moving objects in indoor and outdoor environments in 2017.”
BackgroundA Robust Moving Object Detection in Multi-Scenario Big Data for Video Surveillance
Chen, B.-H., Shi, L.-F., & Ke, X. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2828606 · Cited by 58
Self-citation
Metadata
- Authors
- Chen, B.-H., Shi, L.-F., & Ke, X.
- Affiliations
- Yuan Ze University (TW)
- Fuzhou University (CN)
- Venue
- 2017 IEEE Third International Conference on Multimedia Big Data (BigMM)
- Publisher
- IEEE
- Published
- 2017-04
- Volume
- 11
- Pages
- 134-141
- OpenAlex
- W2732294772
- Cited by
- 7
- References
- 21
- Citations by year
- 2023: 1 · 2020: 1 · 2018: 3 · 2017: 2
- FWCI
- 0.48
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Multimedia Big Data (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- moving object detection, video surveillance, low-rank representation, big data, cloud storage
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