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NIC: A Robust Background Extraction Algorithm for Foreground Detection in Dynamic Scenes
Open accessAbstract
This paper presents a robust foreground detection method capable of adapting to different motion speeds in scenes. A key contribution of this paper is the background estimation using a proposed novel algorithm, neighbor-based intensity correction (NIC), that identifies and modifies the motion pixels from the difference of the background and the current frame. Concretely, the first frame is considered as an initial background that is updated with the pixel intensity from each new frame based on the examination of neighborhood pixels. These pixels are formed into windows generated from the background and the current frame to identify whether a pixel belongs to the background or the current frame. The intensity modification procedure is based on the comparison of the standard deviation values calculated from two pixel windows. The robustness of the current background is further measured using pixel steadiness as an additional condition for the updating process. Finally, the foreground is detected by the background subtraction scheme with an optimal threshold calculated by the Otsu method. This method is benchmarked on several well-known data sets in the object detection and tracking domain, such as CAVIAR 2004, AVSS 2007, PETS 2009, PETS 2014, and CDNET 2014. We also compare the accuracy of the proposed method with other state-of-the-art methods via standard quantitative metrics under different parameter configurations. In the experiments, NIC approach outperforms several advanced methods on depressing the detected foreground confusions due to light artifact, illumination change, and camera jitter in dynamic scenes.
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Robust preprocessing techniques, including foreground and background subtraction, feature extraction, and image segmentation, are critical to maintaining high detection accuracy.
“The need for robust preprocessing techniques, including foreground and background subtraction, feature extraction, and image segmentation, is critical to maintaining high detection accuracy [91].”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [91]
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Metadata
- Authors
- Huynh-The, T., Banos, O., Lee, S., Kang, B. H., Kim, E.-S., & Le-Tien, T.
- Affiliations
- Kyung Hee University (KR)
- University of Tasmania (AU)
- Kwangwoon University (KR)
- Ho Chi Minh City University of Technology (VN)
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2017-07
- Volume
- 27
- Issue
- 7
- Pages
- 1478-1490
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W2320044133
- Cited by
- 51
- References
- 58
- Citations by year
- 2025: 4 · 2024: 2 · 2023: 2 · 2022: 7 · 2021: 5 · 2020: 4 · 2019: 11 · 2018: 8 · 2017: 6
- FWCI
- 6.11
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Locations
- IEEE Transactions on Circuits and Systems for Video Technology (publishedVersion)
- eCite Digital Repository (University of Tasmania) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- University of Twente Research Information (submittedVersion, other-oa)
- University of Twente Research Information (submittedVersion)
- Funders
- National Research Foundation of Korea within the Ministry of Science, ICT and Future Planning through the Korean Government (NRF-2014R1A2A2A01003914)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Infrared Target Detection Methodologies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- foreground detection, background subtraction, dynamic scenes, background estimation, Otsu thresholding, illumination variation, camera jitter, moving object detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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