The DOI matches its Crossref record; 28 of the 29 references it cites were found in a registry or an index.
Unsupervised Anomaly Detection in Traffic Surveillance Based on Global Foreground Modeling
Abstract
One of the main challenges facing foreground detection methods in the applications of video analytic, is the continuous detection of the objects after they have stopped moving. In this paper, a novel global foreground modeling (GFM) guided background subtraction method, which is capable of detecting stopped foreground objects, is presented. Specifically, the GFM method, which detects both the moving and stopped foreground objects, guides the background modeling method, which detects only the moving objects, for the purpose of adaptive background updating. The main contributions of the paper are two-fold. First, a novel GFM guided background modeling method is proposed by adaptively updating the background model based on the differences between the two foreground masks produced by the GFM method and a background modeling method, such as Zivkovic's method, respectively. As a result, the proposed method both improves the foreground detection and detects the stopped foreground objects. Second, a boosting strategy is integrated into the proposed method for eliminating the false alarms caused by noise. Real traffic videos are used to evaluate the effectiveness of the proposed method. In particular, experimental results using the real traffic videos from the New Jersey Department of Transportation (NJDOT) show that the proposed GFM guided background modeling method is able to detect stopped foreground objects, such as stopped vehicles, in real time.
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In the citing paper
“For example, it can be used to determine whether a car is parked on the road accurately even in the circumstance of nearby tree shadows and poor lighting conditions.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [159]
In the citing paper
“Employing an unsupervised guided background modeling approach can help enhance background updates and improve the accuracy of foreground detection [159].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [159]
Citations
Cites
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Živković, Z. (2004) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2004.1333992 · Cited by 2,087
Efficient adaptive density estimation per image pixel for the task of background subtraction
Živković, Z., & van der Heijden, F. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.11.005 · Cited by 1,583
Statistical Pattern Recognition
Webb, A. R. (2002) · Wiley eBooks · doi:10.1002/0470854774 · Cited by 1,460
A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos
Sobral, A. C., & Vacavant, A. (2014) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.12.005 · Cited by 656
Evaluation of background subtraction techniques for video surveillance
Brutzer, S., Höferlin, B., & Heidemann, G. (2011) · doi:10.1109/cvpr.2011.5995508 · Cited by 630
Background segmentation with feedback: The Pixel-Based Adaptive Segmenter
Hofmann, M. R., Tiefenbacher, P., & Rigoll, G. (2012) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2012.6238925 · Cited by 583
Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey
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Research works printed but not linked: 2
- ghahremannezhad, 2020, A real time accident detection framework for traffic video analysis, Proceedings of the Machine Learning and Data Mining in Pattern Recognition - MLDM, 77
- faruque, 2019, Vehicle classification in video using deep learning, Proceedings of the Machine Learning and Data Mining in Pattern Recognition - MLDM, 117
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
Object Detection in Traffic Videos: A Survey
Ghahremannezhad, H., Shi, H., & Liu, C. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3258683 · Cited by 126
Self-citationEffective Machine-Learning Based Traffic Surveillance Moving Vehicle Detection
Alanazi, J. M. (2023) · Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications · doi:10.58346/jowua.2023.i1.008 · Cited by 18
Graph-based Moving Object Segmentation for underwater videos using semi-supervised learning
Kapoor, M., Prummel, W., Giraldo, J. H., Subudhi, B. N., Zakharova, A., Bouwmans, T., & Bansal, A. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104290 · Cited by 16
A Review of Unsupervised Learning Based Anomaly Detection in Traffic Videos
Buchade, S., & Sakarkar, G. (2025) · International Conference on Emerging Trends in Engineering and Technology (ICETET) · doi:10.1109/icetetsip64213.2025.11156587 · Cited by 1
Metadata
- Authors
- Shi, H., Ghahremannezhad, H., & Liu, C.
- Affiliations
- Innovative AI Technologies,Newark,NJ,USA,07103
- New Jersey Institute of Technology (US)
- Venue
- 2022 IEEE International Conference on Imaging Systems and Techniques (IST)
- Publisher
- IEEE
- Published
- 2022-06-21
- Pages
- 1-6
- OpenAlex
- W4286277823
- Cited by
- 6
- References
- 29
- Citations by year
- 2025: 3 · 2023: 3
- FWCI
- 0.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Imaging Systems and Techniques (IST) (publishedVersion)
- 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
- unsupervised anomaly detection, traffic monitoring, background subtraction, boosting, false alarm reduction, video analysis, real-time video processing, stopped vehicles
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