The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
A Robust Moving Object Detection in Multi-Scenario Big Data for Video Surveillance
Abstract
Advanced wireless imaging sensors and cloud data storage contribute to video surveillance by enabling the generation of large amounts of video footage every second. Consequently, surveillance videos have become one of the largest sources of unstructured data. Because multi-scenario surveillance videos are often continuously produced, using these videos to detect moving objects is challenging for the conventional moving object detection methods. This paper presents a novel model that harnesses both sparsity and low-rankness with contextual regularization to detect moving objects in multi-scenario surveillance data. In the proposed model, we consider moving objects as a contiguous outlier detection problem through the use of low-rank constraint with contextual regularization, and we construct dedicated backgrounds for multiple scenarios using dictionary learning-based sparse representation, which ensures that our model can be effectively applied to multi-scenario videos. Quantitative and qualitative assessments indicate that the proposed model outperforms existing methods and achieves substantially more robust performance than the other state-of-the-art methods.
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In the citing paper
“Consequently, the system's overall performance enhances up to 95% accuracy in 60 scenarios, which enables the model to simultaneously detect multiple moving objects with exceptional precision, surpassing the capabilities of the previous system.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [97]
In the citing paper
“The framework proposed in [97] uses an ISTL within unsupervised deep learning to classify abnormal actions.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [97]
In the citing paper
“Therefore, all new incoming actions can be classified using updated values.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [97]
In the citing paper
“The unique aspect of this learner is that it dynamically adjusts and re-determines the anomaly value based on the characteristics of the data.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [97]
Citations
Cites
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Natarajan, B. K. (1995) · SIAM Journal on Computing · doi:10.1137/s0097539792240406 · Cited by 2,817
ViBe: A Universal Background Subtraction Algorithm for Video Sequences
Barnich, O., & Droogenbroeck, M. V. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2101613 · Cited by 1,893
Sparse Reconstruction by Separable Approximation
Wright, S. J., Nowak, R. D., & Figueiredo, M. A. T. (2009) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2009.2016892 · Cited by 1,859
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
SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity
St-Charles, P.-L., Bilodeau, G.-A., & Bergevin, R. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2378053 · Cited by 660
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
Bouwmans, T., & Zahzah, E.-H. (2014) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.11.009 · Cited by 466
Research works printed but not linked: 2
- donoho, 2000, High-dimensional data analysis: The curses and blessings of dimensionality, Proc AMS Conf Math Challenges of the 21st Century, 1
- zhou, 2011, GoDec: Randomized low-rank & sparse matrix decomposition in noisy case, Proc 28th Int Conf Mach Learn (ICML), 33
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
“Furthermore, they introduced sparse and low-rank representation with contextual regularization (SLRC) in 2019 [97].”
BackgroundMethodA Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets
Shidik, G. F., Noersasongko, E., Nugraha, A., Andono, P. N., Jumanto, J., & Kusuma, E. J. (2019) · IEEE Access · doi:10.1109/access.2019.2955387 · Cited by 114
Metadata
- Authors
- Chen, B.-H., Shi, L.-F., & Ke, X.
- Affiliations
- Yuan Ze University (TW)
- Fuzhou University (CN)
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2019-04
- Volume
- 29
- Issue
- 4
- Pages
- 982-995
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W2802932252
- Cited by
- 58
- References
- 36
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 4 · 2023: 7 · 2022: 9 · 2021: 8 · 2020: 11 · 2019: 7
- FWCI
- 3.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Circuits and Systems for Video Technology (publishedVersion)
- Funders
- Ministry of Science and Technology, Taiwan (MOST 106-2221-E-155-066, MOST 106-2218-E-155-007, MOST 105-2218-E-155-003, MOST 105-2218-E-155-010)
- National Natural Science Foundation of China (61502105, 61672159)
- Fujian Collaborative Innovation Center for Big Data Application in Governments
- Technology Innovation Platform Project of Fujian Province (2014H2005)
- Technology Guidance Project of Fujian Province (2017H0015)
- Fujian Province High School Science Fund for Distinguished Young Scholars (JA15075)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Infrared Target Detection Methodologies
- Keywords
- moving object detection, video surveillance, low-rank constraint, sparse representation, dictionary learning, background modeling, outlier detection, big data
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- Chen, B.-H., Shi, L.-F., & Ke, X. (2019). A Robust Moving Object Detection in Multi-Scenario Big Data for Video Surveillance. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/tcsvt.2018.2828606
