Background subtraction in real applications: Challenges, current models and future directions
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When integrated with ML, background subtraction algorithms can adapt to gradual changes in lighting and background shifts.
“When integrated with ML, they can adapt to gradual changes in lighting and background shifts.”
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 [61]
Background subtraction algorithms are suitable for fixed-point passenger counting where the camera is stationary, and background subtraction is effective in relatively stable environments like bus stops or entry gates.
“They are suitable for fixed-point passenger counting where the camera is stationary, and background subtraction is effective in relatively stable environments like bus stops or entry gates.”
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 [61]
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- Authors
- Garcia-Garcia, B., Bouwmans, T., & Rosales Silva, A. J.
- Affiliations
- Laboratoire Mathématiques, Image et Applications (FR)
- Corresponding author
- Bouwmans, Thierry
- ORCID
- Bouwmans, Thierry
- Venue
- Computer Science Review
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 35
- Pages
- 100204
- ISSN
- 1574-0137 (ISSN-L)
- OpenAlex
- W2983488121
- Cited by
- 337
- References
- 534
- Citations by year
- 2026: 16 · 2025: 40 · 2024: 47 · 2023: 65 · 2022: 71 · 2021: 46 · 2020: 41 · 2019: 10 · 2018: 1
- FWCI
- 18.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computer Science Review (acceptedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- Consejo Nacional de Ciencia y Tecnología
- 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 › Engineering › Aerospace Engineering › Infrared Target Detection Methodologies
- Keywords
- background subtraction, moving object detection, foreground-background separation, traffic monitoring, background modeling, omnidirectional camera, CCD detector
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