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Background subtraction in real applications: Challenges, current models and future directions
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“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]
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“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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2015 IEEE International Conference on Fuzzy Systems
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2015 IEEE International Symposium on Information Theory (ISIT)
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Adaptive Color Background Modeling for Real-Time Segmentation of Video Streams*
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Small Moving Vehicle Detection in a Satellite Video of an Urban Area
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GPU Implementation of Extended Gaussian Mixture Model for Background Subtraction
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Maritime surveillance: Tracking ships inside a dynamic background using a fast level-set
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Online Stochastic Tensor Decomposition for Background Subtraction in Multispectral Video Sequences
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Self-citationChen, B., & Huang, S.-C. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2298377 · Cited by 82
An automatic drowning detection surveillance system for challenging outdoor pool environments
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Animal Scanner: Software for classifying humans, animals, and empty frames in camera trap images
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Highly Accurate Moving Object Detection in Variable Bit Rate Video-Based Traffic Monitoring Systems
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Error Bounded Foreground and Background Modeling for Moving Object Detection in Satellite Videos
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Self-citationDesign and Implementation of an RFID-Based Customer Shopping Behavior Mining System
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Background Subtraction With Real-Time Semantic Segmentation
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Self-citationBackground Subtraction with Dirichlet Processes
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Highway Traffic Congestion Classification using Holistic Properties
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A 3D Atrous Convolutional Long Short-Term Memory Network for Background Subtraction
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Self-citationMotion-Aware Graph Regularized RPCA for background modeling of complex scenes
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Adaptive Deep Convolutional Neural Networks for Scene-Specific Object Detection
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RetailNet: A Deep Learning Approach for People Counting and Hot Spots Detection in Retail Stores
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Background Subtraction For Visual Surveillance: A Fuzzy Approach
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Understanding fish behavior during typhoon events in real-life underwater environments
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Dynamic Background Learning through Deep Auto-encoder Networks
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Cited by
Sorted by citations
A survey on deep learning and its applications
Dong, S., Wang, P., & Abbas, K. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100379 · Cited by 1,622
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Self-citationDeep Dense Multi-Scale Network for Snow Removal Using Semantic and Depth Priors
Zhang, K., Li, R., Yu, Y., Luo, W., & Li, C. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3104166 · Cited by 387
Rank-One Prior: Real-Time Scene Recovery
Liu, J., Liu, R. W., Sun, J., & Zeng, T. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3226276 · Cited by 163
Moving objects detection with a moving camera: A comprehensive review
Chapel, M.-N., & Bouwmans, T. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100310 · Cited by 130
Self-citation
Metadata
- 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
- 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 › Computer Science › Computer Vision and Pattern Recognition › 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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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.
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