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
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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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