Awesome
awesome-background-subtraction
A curated list of background subtraction papers and related applications resources
Upcoming Deadlines for Computer Vision Conferences
- CVPR - 16 November
Contents
- Deep Learning based Papers
- GAN Based Papers
- Non-Deep Learning based Papers
- Review/survey papers
- Datasets
- Awesome Researchers
- Awesome Resources
- Projects
Deep Learning based Papers
2021 Papers, 2020 Papers, 2019 Papers, 2018 Papers, 2017 Papers, 2016 Papers
2021 Papers
- 2021 - An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs (IEEE Transactions on Intelligent Transportation System)
- 2021 - BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction (IEEE Access)
- 2021 - Multi-Frame Recurrent Adversarial Network for Moving Object Segmentation (CVPR-2021)
- 2021 - Deep Adversarial Network for Scene Independent Moving Object Segmentation (IEEE Signal Processing Letters)
- 2021 - End-to-End Recurrent Generative Adversarial Network for Traffic and Surveillance Applications (IEEE Transactions on Vehicular Technology)
2020 Papers
- 2020 - Graph Moving Object Segmentation (2020 - IEEE Transactions on Pattern Analysis and Machine Intelligence) Source Code
- 2020 - Scene Independency Matters: An Empirical Study of Scene Dependent and Scene Independent Evaluation for CNN-Based Change Detection (2020 - IEEE Transactions on Intelligent Transportation Systems)
- 2020 - 3DCD: A Scene Independent End-to-End Spatiotemporal Feature Learning Framework for Change Detection in Unseen Videos(2020 - IEEE Transactions on Image Processing)
- 2020 - MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos (ACM-MM 2020)
- 2020 - An End-to-End Edge Aggregation Network for Moving Object Segmentation (CVPR-2020)
- 2020 - MotionRec: Unified Deep Framework for Moving Object Recognition (WACV 2020) Source Code
- 2020 - BSUV-Net: A Fully-Convolutional Neural Network for Background Subtraction of Unseen Videos(WACV 2020)Source Code
- 2020 - Semi-supervised Background Subtraction of Unseen Videos: Minimization of the Total Variation of Graph Signals (ICIP 2020)
2019 Papers
Journals
- 2019 - 3DFR: A Swift 3D Feature Reductionist Framework for Scene Independent Change Detection (2019 - IEEE Signal Processing Letters)
- 2019 - vsEnDec: An improved image to image CNN for foreground localization (2019 - IEEE Transactions on Intelligent Transportation Systems)
- 2019 - Deep neural network concepts for background subtraction: A systematic review and comparative evaluation (2019 - Neural Networks, Elsevier)
- 2019 - Panoramic Background Image Generation for PTZ Cameras (2019 - IEEE Transactions on Image Processing)
- 2019 - Moving Object Detection Through Image Bit-Planes Representation Without Thresholding (2019 - IEEE Transactions on Intelligent Transportation Systems)
- 2019 - Rapid and Robust Background Modeling Technique for Low-Cost Road Traffic Surveillance Systems (2019 - IEEE Transactions on Intelligent Transportation Systems)
- 2019 - Video Foreground Extraction Using Multi-View Receptive Field and Encoder–Decoder DCNN for Traffic and Surveillance Applications (2019 - IEEE Transactions on Vehicular Technology)
- 2019 - Foreground Segmentation Using Adaptive 3 Phase Background Model (2019 - IEEE Transactions on Intelligent Transportation Systems)
- 2019 - A 3D CNN-LSTM-Based Image-to-Image Foreground Segmentation (2019 - IEEE Transactions on Intelligent Transportation Systems)
- 2019 - Salient Features for Moving Object Detection in Adverse Weather Conditions during Night Time (2019 - IEEE Transactions on Circuits and Systems for Video Technology)
- 2019 - Illumination-Aware Multi-Task GANs for Foreground Segmentation (2019 - IEEE Access)
- 2019 - Moving object detection in complex scene using spatiotemporal structured-sparse RPCA (2019 - IEEE Transactions on Image Processing)
- 2019 - Refining background subtraction using consistent motion detection in adverse weather (2019 - Journal of Electronic Imaging)
- 2019 - DeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences (2019 - Arxiv)
- 2019 - Combining Background Subtraction Algorithms with Convolutional Neural Network (2019 - Journal of Electronic Imaging)
Conferences
- 2019 - An end-to-end deep learning approach for simultaneous background modeling and subtraction (BMVC 2019)
- 2019 - Simple background subtraction constraint for weakly supervised background subtraction network (AVSS 2019)
- 2019 - Unsupervised moving object detection via contextual information separation (CVPR 2019)
- 2019 - Learning to See Moving Objects in the Dark (ICCV - 2019)
- 2019 - Motion Saliency Based Generative Adversarial Network for Underwater Moving Object Segmentation (ICIP 2019)
- 2019 - FgGAN: A Cascaded Unpaired Learning for Background Estimation and Foreground Segmentation (WACV 2019)
- 2019 - Robust Change Captioning (CVPR-2019)
- 2019 - Panoramic Video Separation with Online Grassmannian Robust Subspace Estimation (ICCV 2019)
- 2019 - Rapid Technique to Eliminate Moving Shadows for Accurate Vehicle Detection (WACV 2019)
- 2019 - Online and batch supervised background estimation via L1 regression (WACV 2019)
- 2019 - TU-VDN: Tripura University Video Dataset at Night Time in Degraded Atmospheric Outdoor Conditions for Moving Object Detection (ICIP 2019)
- 2019 - Detection of Dynamic Objects in Videos Using LBSP and Fuzzy Gray Level Difference Histograms (FUZZ-2019)
- 2019 - Moving Object Detection Under Discontinuous Change in Illumination Using Tensor Low-Rank and Invariant Sparse Decomposition (CVPR - 2019)
2018 Papers
Journals
- 2018 - MSFgNet: A Novel Compact End-to-End Deep Network for Moving Object Detection (2019 - IEEE Transactions on Intelligent Transportation Systems)
- 2018 - A Foreground Inference Network for Video Surveillance Using Multi-View Receptive Field (Arxiv-2018)
- 2018 - Change Detection by Training a Triplet Network for Motion Feature Extraction (2018 - IEEE Transactions on Circuits and Systems for Video Technology)
- 2018 - Multiscale Fully Convolutional Network for Foreground Object Detection in Infrared Videos (2018 - IEEE Geoscience and Remote Sensing Letters)
- 2018 - Foreground Segmentation Using a Triplet Convolutional Neural Network for Multiscale Feature Encoding, Source Code (2018-Arxiv)
- 2018 - Deep Background Modeling Using Fully Convolutional Network (2018- IEEE Transactions on Intelligent Transportation Systems)
- 2018 - A deep convolutional neural network for video sequence background subtraction (2018-Pattern Recognition, Elsevier)
- 2018 - Foreground segmentation using convolutional neural networks for multiscale feature encoding (2018-Pattern Recognition Letters, Elsevier)
- 2018 - A 3D Atrous Convolutional Long Short-Term Memory Network for Background Subtraction (2018-IEEE Access)
- 2018 - A novel framework for background subtraction and foreground detection (2018-Pattern Recognition, Elsevier)
- 2018 - Learning Multi-scale Features for Foreground Segmentation (2018-Arxiv)
- 2018 - MPNET: An End-to-End Deep Neural Network for Object Detection in Surveillance Video (2018-IEEE Access)
Conference
- 2018 - CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction (ICPR-2018)
- 2018 - Learning Background Subtraction by Video Synthesis and Multi-scale Recurrent Networks (ACCV-2018)
- 2018 - Multi-scale Recurrent Encoder-Decoder Network for Dense Temporal Classification (ICPR-2018)
- 2018 - MsEDNet: Multi-Scale Deep Saliency Learning for Moving Object Detection (SMC-2018)
- 2018 - BSCGAN: Deep Background Subtraction with Conditional Generative Adversarial Networks (ICIP-2018)
- 2018 - Foreground Detection in Surveillance Video with Fully Convolutional Semantic Network (ICIP-2018)
- 2018 - Local Compact Binary Patterns for Background Subtraction in Complex Scenes (ICPR-2018)
- 2018 - A Co-occurrence Background Model with Hypothesis on Degradation Modification for Object Detection in Strong Background Changes (ICPR-2018)
- 2018 - Background Subtraction via 3D Convolutional Neural Networks (ICPR-2018)
- 2018 - ReMotENet Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos (WACV-2018)
2017 Papers
- 2017 - End-to-end video background subtraction with 3D convolutional neural networks
- 2017 - Foreground Segmentation for Anomaly Detection in Surveillance Videos Using Deep Residual Networks, Source Code
- 2017 - Learning deep structured network for weakly supervised change detection
- 2017 - A Deep Convolutional Neural Network for Background Subtraction
- 2017 - Analytics of deep neural network in change detection
- 2017 - Background modelling based on generative unet
- 2017 - Background subtraction using encoder-decoder structured convolutional neural network
- 2017 - FusionSeg Learning to combine motion and appearance for fully automatic segmention of generic objects in videos, Source Code
- 2017 - Interactive deep learning method for segmenting moving objects, Source Code
- 2017 - Joint Background Reconstruction and Foreground Segmentation via a Two-Stage Convolutional Neural Network
- 2017 - Pixel-wise Deep Sequence Learning for Moving Object Detection
- 2017 - WiSARDrp for Change Detection in Video Sequences (ESANN-2017)
2016 Papers
GAN Based Papers
2018 Papers
- 2019 - Illumination-Aware Multi-Task GANs for Foreground Segmentation
- 2019 - FgGAN A Cascaded Unpaired Learning for Background Estimation and Foreground Segmentation
- 2018 - BSCGAN: Deep Background Subtraction with Conditional Generative Adversarial Networks
Non-Deep Learning based Papers
Landmark Papers, 2018 Papers, 2017 Papers, 2016 Papers, 2015 Papers
Landmark Papers in Background Subtraction
- 2020 - Scene Independency Matters: An Empirical Study of Scene Dependent and Scene Independent Evaluation for CNN-Based Change Detection (2020 - IEEE Transactions on Intelligent Transportation Systems)
- 2015 - SuBSENSE - A Universal Change Detection Method With Local Adaptive Sensitivity
- 2012 - PBAS - Background Segmentation with Feedback: The Pixel-Based Adaptive Segmenter
- 2011 - ViBe: A Universal Background Subtraction Algorithm for Video Sequences
- 2006 - A Texture-Based Method for Modeling the Background and Detecting Moving Objects
- 1999 - Adaptive background mixture models for real-time tracking
2018 Papers
- 2018 - A Background Modeling and Foreground Detection Algorithm Using Scaling Coefficients Defined With a Color Model Called Lightness-Red-Green-Blue
- 2018 - M4CD A Robust Change Detection Method for Intelligent Visual Surveillance
- 2018 - CANDID:Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction
- 2018 - ANTIC: ANTithetic Isomeric Cluster Patterns for Medical Image Retrieval and Change Detection
- 2018 - Multi-scale Recurrent Encoder-Decoder Network for Dense Temporal Classification
- 2018 - A New Foreground Segmentation Method for Video Analysis in Different Color Spaces
- 2018 - Background subtraction via 3D convolutional neural networks
- 2018 - Local Compact Binary Patterns for Background Subtraction in Complex Scenes
- 2018 - Unsupervised deep context prediction for background estimation and foreground segmentation
2017 Papers
Review/survey Papers
- 2021 - An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs (IEEE Transactions on Intelligent Transportation System)
- 2019 - Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
- 2018 - New trend in video foreground detection using deep learning
- 2014 - Traditional and recent approaches in background modeling for foreground detection: An overview
Datasets
- Change Detection Net (CDNet)
- Scene Back Modelling (SBMNet)
- SBI
- SBM-RGBD
- Wallflower
- fish4knowledge
- MARDCT
- MuHavi
- LASIESTA
Awesome Researchers
Awesome Resources
Projects
- [Background subtraction using deep learning method by Yiqi Yan](https://github.com/SaoYan/bgsCNN)
Contributions are always welcomed!
If you have any suggestions (missing papers, projects, source code, new papers, key researchers, dataset, etc.), please feel free to edit and pull a request. (or just let me know the title of paper)