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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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Pixel-based heterogeneous traffic measurement considering shadow and illumination variation
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Panning and Jitter Invariant Incremental Principal Component Pursuit for Video Background Modeling
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Moving object detection using self adaptive Gaussian Mixture Model for real time applications
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Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors
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Motion background modeling based on context-encoder
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Moving vehicle detection based on fuzzy background subtraction
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Tracking of Multiple Honey Bees on a Flat Surface
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Practical ReProCS for separating sparse and low-dimensional signal sequences from their sum — Part 1
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Detection of swimmer using dense optical flow motion map and intensity information
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Detecting Dynamic Objects with Multi-view Background Subtraction
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Real-time hand gesture recognition for augmented screen using average background and camshift
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Fire-Flame Detection Based on Fuzzy Finite Automation
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Human Body Model Acquisition and Motion Capture Using Voxel Data
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A model based method of pedestrian abnormal behavior detection in traffic scene
Qianyin, J., Li, G., Yu, J., & Li, X. (2015) · IEEE International Smart Cities Conference (ISC2) · doi:10.1109/isc2.2015.7366164 · Cited by 18
Temporally Adaptive Restricted Boltzmann Machine for Background Modeling
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Practical automatic background substitution for live video
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Superpixels based Manifold Structured Sparse RPCA for Moving Object Detection
Javed, S., Mahmood, A., Bouwmans, T., & Soon, K. J. (2017) · HAL · Cited by 18
Self-citationSelf-organizing background subtraction using color and depth data
Maddalena, L., & Petrosino, A. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6741-7 · Cited by 18
Hardware implementation of the PBAS foreground detection method in FPGA
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PID-based regulation of background dynamics for foreground segmentation
Tiefenbacher, P., Hofmann, M. R., Merget, D., & Rigoll, G. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025664 · Cited by 17
Multi-scale multi-feature codebook-based background subtraction
Zaharescu, A., & Jamieson, M. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130461 · Cited by 17
Ship Tracking Using Background Subtraction and Inter-Frame Correlation
Zhang, S., Qi, Z., & Zhang, D. (2009) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2009.5302115 · Cited by 17
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Wahyono, W., Filonenko, A., & Jo, K.-H. (2015) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2015.7392432 · Cited by 17
Shrinking Encoding with Two-Level Codebook Learning for Fine-Grained Fish Recognition
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GMM Background Modeling Using Divergence-Based Weight Updating
Pulgarin-Giraldo, J. D., Álvarez-Meza, A. M., Insuasti-Ceballos, D., Bouwmans, T., & Castellanos-Dominguez, G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-52277-7_35 · Cited by 17
Self-citationChallenges In Video Based Object Detection In Maritime Scenario Using Computer Vision
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Importance of detection for video surveillance applications
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Improved visual background extractor using an adaptive distance threshold
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Detecting shopper groups in video sequences
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Visual Surveillance for Airport Monitoring Applications
Aguilera, J., Thirde, D., Kampel, M., Borg, M. G., Fernández, G. J., & Ferryman, J. M. (2006) · CentAUR (University of Reading) · Cited by 16
How many fish in a tank? Constructing an automated fish counting system by using PTV analysis
Abe, S., Takagi, T., TAKEHARA, K., KIMURA, N., Hiraishi, T., Komeyama, K., Torisawa, S., & Asaumi, S. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2270627 · Cited by 16
Segmentation of sows in farrowing pens
Tu, G. J., Karstoft, H., Pedersen, L. J., & Jørgensen, E. (2013) · IET Image Processing · doi:10.1049/iet-ipr.2012.0734 · Cited by 15
Arshad, N., Moon, K.-S., & Kim, J. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.913463 · Cited by 15
A Comparison between Background Modelling Methods for Vehicle Segmentation in Highway Traffic VideosPreprint
Marcomini, L. A., & Cunha, A. L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.02835 · Cited by 15
2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
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Improvement of a neural-fuzzy motion detection vision model for complex scenario conditions
Chacon-Murguia, M. I., Ramírez-Alonso, G. M. D. J., & Gonzalez-Duarte, S. (2013) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2013.6706734 · Cited by 14
Macro-Block-Level Selective Background Difference Coding for Surveillance Video
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Video Segmentation via a Gaussian Switch Background Model and Higher Order Markov Random Fields
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Visual Monitoring of Driver and Passenger Control Panel Interactions
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Efficient local monitoring approach for the task of background subtraction
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LiveCap: Real-time Human Performance Capture from Monocular VideoPreprint
Habermann, M., Xu, W., Zollhöfer, M., Pons‐Moll, G., & Theobalt, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.02648 · Cited by 14
A novel video coding scheme using a scene adaptive non-parametric background model
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Background Modeling Algorithm Based on Transitions Intensities
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Foreground Detection by Robust PCA Solved via a Linearized Alternating Direction Method
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Self-citationPerformance Evaluation of the Parallel Codebook Algorithm for Background Subtraction in Video Stream
Szwoch, G. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-21512-4_18 · Cited by 13
Hand Gesture Recognition Using Modified 1$ and Background Subtraction Algorithms
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Three-level GPU accelerated Gaussian mixture model for background subtraction
Yin, L., Wang, G., & Lin, X. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.906385 · Cited by 13
Moving object detection from moving platforms using Lagrange multiplier
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The detection of moving objects in video by background subtraction using Dempster-Shafer theory
Munteanu, O., Bouwmans, T., Zahzah, E.-H., & Vasiu, R. (2015) · Cited by 13
Self-citationAli, I., Mille, J., & Tougne, L. (2013) · Pattern Analysis and Applications · doi:10.1007/s10044-013-0346-6 · Cited by 12
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Real-time background estimation of traffic imagery using group-based histogram
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A visual sensing platform for creating a smarter multi-modal marine monitoring network
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Dual‐rate background subtraction approach for estimating traffic queue parameters in urban scenes
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Pattern-based video coding with dynamic background modeling
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Fuzzy foreground detection for infrared videos
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Self-citationMoving object detection using genetic algorithm for traffic surveillance
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Adaptive Foreground Extraction for Deep Fish Classification
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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
Bouwmans, T., Javed, S., Sultana, M., & Jung, S. K. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.04.024 · Cited by 412
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
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.
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