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Cited by 6Video Surveillance and Tracking MethodsAnomaly Detection Techniques and ApplicationsInfrared Target Detection Methodologies

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

Shi, Hang, Ghahremannezhad, Hadi, Liu, Chengjun·2022·2022 IEEE International Conference on Imaging Systems and Techniques (IST)
DOI 10.1109/ist55454.2022.9827759
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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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Crossref · 10.1109/ist55454.2022.9827759

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Research works printed but not linked: 2

  1. 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
  2. faruque, 2019, Vehicle classification in video using deep learning, Proceedings of the Machine Learning and Data Mining in Pattern Recognition - MLDM, 117

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Metadata

Authors
Shi, H., Ghahremannezhad, H., & Liu, C.
Affiliations
  • Innovative AI Technologies,Newark,NJ,USA,07103
  • New Jersey Institute of Technology (US)
ORCID
Venue
2022 IEEE International Conference on Imaging Systems and Techniques (IST)
Publisher
IEEE
Published
2022-06-21
Pages
1-6
DOI
10.1109/ist55454.2022.9827759
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
Licence
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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