Background Modeling via Deep Learning
Note: This list of publications comes from my research. Please cite my following paper:
T. Bouwmans, S. Javed, M. Sultana, S. Jung, “Deep Neural Network Concepts in Background Subtraction: A Systematic Review and A Comparative Evaluation”, Neural Networks, 2019.
Restricted Botzman Machine (1 paper)
J. Gracewell, M. John, "Dynamic background modeling using deep learning autoencoder network", Multimedia Tools and Applications, pages 1-21, March 2019.
Deep Encoder-Decoder (12 papers)
S. Choo, W. Seo, D. Jeong, N. Cho, "Multi-scale Recurrent Encoder-Decoder Network for Dense Temporal Classification", IAPR International Conference on Pattern Recognition, ICPR 2018, Beijing, China, 2018.
S. Choo, W. Seo, D. Jeong, N. Cho, "Learning Background Subtraction by Video Synthesis and Multi-scale Recurrent Networks", Asian Conference on Computer Vision, ACCV 2018, Perth, Australia, December 2018.
A. Farnoosh, B. Rezaei, S. Ostadabbas, “DEEPBM: Deep Probabilistic Background Model Estimation from Video Sequences”, International Workshop on Deep Learning for Pattern Recognition (DLPR 2020) in conjunction with the International Conference on Pattern Recognition, ICPR 2020, Milan, Italy, January 2021. [Low-rank]
B. Rezaei, A. Farnoosh, S. Ostadabbas, "G-LBM : Generative Low-dimensional Background Model Estimation from Video Sequences", Preprint, March 2020. [Low-rank]
B. Rezaei, A. Farnoosh, S. Ostadabbas, "G-LBM : Generative Low-dimensional Background Model Estimation from Video Sequences", European Conference on Computer Vision, ECCV 2020, pages 293–310, October 2020. [Low-rank]
A. Akhriev, J. Marecek, "Deep Autoencoders with Value-at-RiskThresholding for Unsupervised Anomaly Detection", Preprint, 2019.
P. Patil, A. Dudhane, S. Murala, A. Gonde, "A Novel Saliency-Based Cascaded Approach for Moving Object Segmentation", International Conference on Computer Vision and Image Process, CVIP 2019, pages 311-322, 2019.
Y. Li, X. Yu, H. Cao, M. Xu, “An Autoencoder Based Background Subtraction for Public Surveillance”, IEICE Transactions on Fundamentals of Electronics, 2021.
D. Liang, J. Pan, H. Sun, H. Zhou, "Spatio-temporal attention model for foreground detection in cross-scene surveillance videos", MDPI Sensors, Volume 19, No. 29, page 5142, 2019.
A. Kebir, M. Taibi, "End-to-end deep auto-encoder for segmenting a moving object with limited training data", International Journal of Electrical and Computer Engineering, Volume 12, Issue 6, pages 6045-6057, December 2022.
B. Sauvalle, A. Fortelle, "Autoencoder-based background reconstruction and foreground segmentation with background noise estimation", IEEE Winter Conference on Applications of Computer Vision, WACV 2023, January 2023.
D. Liang, D. Zhang, Q. Wang, Z. Wei, L. Zhang, "CrossNet: Cross-scene Background Subtraction Network via 3D Optical Flow", IEEE Transactions on Multimedia, April 2023.
Robust Deep Encoder-Decoder (4 papers)
R. Chalapathy, A. Menon, S. Chawla, "Robust, Deep and Inductive Anomaly Detection", Preprint, 2017.
X. Teng, M. Yan, A. Ertugrul, Y. Lin, "Deep into Hypersphere: Robust and Unsupervised Anomaly Discovery in Dynamic Networks", International Joint Conference on Artificial Intelligence, IJCAI 2018, pages 2724-2730, 2018.
S. Ammar, T. Bouwmans, N. Zaghden, M. Neji, "Moving Objects Segmentation Based on DeepSphere in Video Surveillance", International Symposium on Visual Computing, ISVC 2019, Tahoe City, USA, October 2019.
S. Ammar, T. Bouwmans, N. Zaghden, M. Neji, "A Deep Detector Classifier (DeepDC) for moving objects segmentation and classification in video surveillance", IET Image Processing, 2020.
Fast Encoder-Decoder (2 papers)
B. Hou, Y. Liu, N. Ling, "A Super-Fast Deep Network for Moving Object Detection", IEEE International Symposium on Circuits and Systems, ISCAS 2020, pages 1-5, Sevilla, Spain, 2020.
B. Hou, "Deep Learning-Based Low Complexity and High Efficiency Moving Object Detection Methods", PhD Thesis, Department of Computer Science and Engineering, Santa Clara University, USA, March 2022.
Multi-scale Encoder-Decoder (2 papers)
Y. Yang, D. Li, X. Li, Z. Zhang, G. Xie, “A multi-scale inputs and labels model for background subtraction”, Signal, Image and Video Processing volume 17, pages 4133-414, June 2023.
Y. Yang, T. Xia, D. Li, Z. Zhang, G. Xie, “A multi-scale feature fusion spatial–channel attention model for background subtraction”, Multimedia Systems, July 2023.
Convolutional Neural Networks (CNN) (127 papers)
M. Braham, M. Van Droogenbroeck, “Deep background subtraction with scene-specific convolutional neural networks”, International Conference on Systems, Signals and Image Processing, IWSSIP2016, Bratislava, Slovakia, May 2016.
Y. Wang, Z. Luo, P. Jodoin, “Interactive Deep Learning Method for Segmenting Moving Objects”, Pattern Recognition Letters, Special Issue on “Scene Background Modeling and Initialization”, 2016.
C. Bautista, C. Dy, M. Manalac, R. Orbe, M. Cordel, “Convolutional neural network for vehicle detection in low resolution traffic videos”, TENCON 2016, 2016.
M. Babaee, D. Dinh, G. Rigoll, "A Deep Convolutional Neural Network for Background Subtraction", Pattern Recognition, September 2017.
K. Lim, W. Jang, C. Kim, "Background subtraction using encoder-decoder structured convolutional neural network", IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017, Lecce, Italy, 2017.
L. Pinheiro Cinelli, "Anomaly Detection in Surveillance Videos using Deep Residual Networks", Master Thesis, Universidade de Rio de Janeiro, February 2017.
X. Zhao, Y. Chen, M. Tang, J. Wang, "Joint Background Reconstruction and Foreground Segmentation via A Two-stage Convolutional Neural Network", Preprint, 2017.
X. Li, M. Ye, Y. Liu, C. Zhu, “Adaptive Deep Convolutional Neural Networks for Scene-Specific Object Detection”, IEEE Transactions on Circuits and Systems for Video Technology, September 2017.
T. Minematsu, A. Shimada, R. Taniguchi, “Analytics of deep neural network in change detection", IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017, Lecce, Italy, 2017.
T. Minematsu, A. Shimada, H. Uchiyama, R. Taniguchi,"Analytics of Deep Neural Network-based Background Subtraction", MDPI Journal of Imaging, 2018.
T. Minematsu, A. Shimada, R. Taniguchi, "Simple background subtraction constraint for weakly supervised background subtraction network", IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019, Taipei, Taiwan, 2019.
T. Minematsu, A. Shimada, R. Taniguchi, "Rethinking Background and Foreground in Deep Neural Network-Based Background Subtraction", IEEE International Conference on Image Processing, ICIP 2020, Abu Dhabi, UAE, October 2020.
T. Hamada, T. Minematsu, A. Simada, F. Okubo, Y. Taniguchi, "Background Subtraction Network Module Ensemble for Background Scene Adaptation", IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2022, Madrid, Spain, November 2022.
L. Yang, J. Li, Y. Luo, Y. Zhao, H. Cheng, J. Li, "Deep Background Modeling using Fully Convolutional Network", IEEE Transactions on Intelligent Transportation Systems, 2017.
Y. Chen, J. Wang, B. Zhu, M. Tang, H. Lu, "Pixel-wise Deep Sequence Learning for Moving Object Detection", IEEE Transactions on Circuits and Systems for Video Technology, 2017.
D. Sakkos, H. Liu, J. Han, L. Shao, “End-to-end video background subtraction with 3D convolutional neural networks”, Multimedia Tools and Applications, pages 1-19, December 2017.
L. Lim, H. Keles, "Foreground Segmentation using a Triplet Convolutional Neural Network for Multiscale Feature Encoding", Preprint, January 2018.
L. Lim, H. Keles, "Foreground segmentation using convolutional neural networks for multiscale feature encoding", Pattern Recognition Letters, Volume 112 , pages 256–262, 2018.
L. Lim, l. Ang, H. Keles, "Learning Multi-scale Features for Foreground Segmentation", Preprint, September 2018.
R. Yu, H. Wang, L. Davis, "ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos", Preprint, January 2018.
T. Akilan, "A Foreground Inference Network for Video Surveillance using Multi-View Receptive Field", Preprint, January 2018.
T. Akilan, Q. Wu, "An Improved Video foreground Extraction Strategy using Multi-view Receptive Field and EnDec CNN", IEEE Transactions on Industrial Informatics, 2019.
T. Akilan, Q. Wu, A. Safaei, J. Huo, Y. Yang, "A 3D CNN-LSTM-Based Image-to-Image Foreground Segmentation', IEEE Transactions on Intelligent Transportation Systems, 2019.
T. Akilan, Q. Wu, W. Jiang, A. Safaei, J. Huo, “New Trend in Video Foreground Detection using Deep Learning”, IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2018, Windsor, Canada, pages 889-892, 2018.
T. Akilan, Q. Wu, “Double Encoding - Slow Decoding Image to Image CNN for Foreground Identification with Application Towards Intelligent Transportation”, IEEE Conference on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, pages 395-403, 2018.
T. Akilan, Q. Wu, "sEnDec: An improved image to image CNN for foreground localization", IEEE Intelligent Transportation Systems Transactions, 2019.
T. Akilan, “Video foreground localization from traditional methods to deep learning”, PhD Thesis, University of Windsor, USA, 2018.
T. Akilan, Q. Wu, W. Zhang, “Video foreground extraction using multi-view receptive field and encoder–decoder DCNN for traffic and surveillance applications", IEEE Transactions on Vehicular Technology, Volume 68, No. 10, pages 9478–9493, 2019.
D. Zeng, M. Zhu, "Multiscale Fully Convolutional Network for Foreground Object Detection in Infrared Videos", IEEE Geoscience and Remote Sensing Letters, 2018.
D. Zeng, M. Zhu, "Background Subtraction using Multiscale Fully Convolutional Network", IEEE Access, pages 16010 - 16021, March 2018.
D. Zeng, M. Zhu, A. Kuijper, “Combining Background Subtraction Algorithms with Convolutional Neural Network”, Preprint, 2018.
Z. Hu, T. Turki, N. Phan, J. Wang, "3D Atrous Convolutional Long Short-Term Memory Network for Background Subtraction", IEEE Access, 2018.
X. Liang, S. Liao, X. Wang, W. Liu, Y. Chen, S. Li, "Deep Background Subtraction with Guided Learning", IEEE International Conference on Multimedia and Expo, ICME 2018 San Diego, USA, July 2018.
C. Lin, B. Yan, W. Tan, "Foreground Detection in Surveillance Video with Fully Convolutional Semantic Network", IEEE International Conference on Image Processing, ICIP 2018, pages 4118-4122, Athens, Greece, October 2018.
J. Liao, G. Guo, Y. Yan, H. Wang, "Multiscale Cascaded Scene-Specific Convolutional Neural Networks for Background Subtraction", Pacific Rim Conference on Multimedia, PCM 2018, pages 524-533, 2018.
Y. Yan, H. Zhao, F. Kao, V. Vargas, S. Zhao, J. Ren, "Deep Background Subtraction of Thermal and Visible Imagery for Pedestrian Detection in Videos", International Conference on Brain Inspired Cognitive Systems, BICS 2018, 2018.
B. Weinstein, "Scene-specific convolutional neural networks for video-based biodiversity detection", Methods in Ecology and Evolution, 2018.
C. Zhao, T. Cham, X. Ren, J. Cai, H. Zhu, "Background Subtraction Based on Deep Pixel Distribution Learning", IEEE International Conference on Multimedia and Expo, ICME 2018, San Diego, USA, pages 1-6, 2018.
C. Zhao, A. Basu, "Dynamic Deep Pixel Distribution Learning for Background Subtraction", IEEE Transactions on Circuits and Systems for Video Technology, 2019.
Y. Wang, Z. Yu, L. Zhu, "Foreground Detection with Deeply Learned Multi-scale Spatial-Temporal Features", MDPI Sensors, 2018.
C. Chen, S. Zhang, C. Du, "Learning to Detect Instantaneous Changes with Retrospective Convolution and Static Sample Synthesis", Preprint, 2018.
Y. Gao, H. Cai, X. Zhang, L. Lan, Z. Luo, "Background Subtraction via 3D Convolutional Neural Networks", IAPR International Conference on Pattern Recognition ICPR 2018, pages, 1271-1276, Beijing, China, 2018.
P. Patil, S. Murala ,"MSFgNet: A Novel Compact End-to-End Deep Network for Moving Object Detection", IEEE Transactions on Intelligent Transportation Systems, December 2018.
P. Patil, S. Murala, A. Dhall, S. Chaudhary, "MsEDNet: Multi-Scale Deep Saliency Learning for Moving Object Detection", IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, pages 1670-1675, Miyazaki, Japan, 2018.
D. Le, T. Pham, "Encoder-Decoder Convolutional Neural Network for Change Detection", CITA 2018, 2018.
O. Karadag, O. Erdas, "Evaluation of the robustness of deep features on the change detection problem", IEEE Signal Processing and Communications Applications Conference, SIU 2018, pages 1-4, 2018.
D. Li ,M. Jiang,Y. Fang, Y. Huang, C. Zhao, "Deep Video Foreground Target Extraction with Complex Scenes", IEEE International Conference on Sensor Networks and Signal Processing, SNSP 2018, pages 440-445, 2018.
Y. Chan, "Deep learning-based scene-awareness approach for intelligent change detection in videos", Journal of Electronic Imaging", February 2019.
A. Salman, S. Siddiqui, F. Shafait, A. Mian, M. Shortis, K. Khurshid, A. Ulges, U. Schwanecke, "Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system", ICES Journal of Marine Science, 2019.
T. Lin, J. Yeh, F. Wu, Y. Chuang, A. Dellinger, "An Experiment for Background Subtraction in a Dynamic Scene", Canadian Conference on Artificial Intelligence, pages 420-425, 2019.
M. Vijayan, R. Mohan, P. Raguraman, "Contextual background modeling using deep convolutional neural network", Multimedia Tools and Applications, 2019.
J. Zhang, Y. Li, F. Chen, Z. Pan, X. Zhou, Y. Li, S. Jiao, "X-Net: A Binocular Summation Network for Foreground Segmentation", IEEE Access, 2019.
Y. Yan, H. Zhao, F. Kao, V. Vargas, S. Zhao, J. Ren, "Deep Background Subtraction of Thermal and Visible Imagery for Pedestrian Detection in Videos", BICS 2018, 2018.
T. Yu, J. Yang, W. Lu, "Refinement of Background-Subtraction Methods based on Convolutional Neural Network Features for Dynamic Background", MDPI Algorithms, 2019.
X. Ou, P. Yan, Y. Zhang, B. Tu, G. Zhang, J. Wu, W. Li, "Moving Object Detection Method via ResNet-18 with Encoder.-Decoder Structure in Complex Scenes", IEEE Access, 2019.
A. Shahbaz, K. Jo, "Deep Foreground Segmentation using Convolutional Neural Network", IEEE International Symposium on Industrial Electronics, ISIE 2019, pages 1397-1400, Vancouver, Canada, 2019.
Y. Yang , T. Zhang, J. Hu, D. Xu, G. Xie, "End-to-End Background Subtraction via a Multi-Scale Spatio-Temporal Model", IEEE Access, 2019.
M. Qiu, X. Li, "A Fully Convolutional Encoder–Decoder Spatial–Temporal Network for Real-Time Background Subtraction", IEEE Access, 2019.
C. Chen, S. Zhang, C. Du, "Retrospective convolution and static sample synthesis for instantaneous change detection", International Workshop on Pattern Recognition, July 2019.
C. Chen, S. Zhang, "Spatio-Temporal Neural Network with Dilated Retrospective Convolution for Short-Time Change Detection", International Conference on Information, Communication and Networks, ICICN 2019, pages 213-218, Macao, Macao, 2019.
J. Wang, K. Chan, "Background subtraction based on Encoder-Decoder Structured CNN", Asian Conference on Pattern Recognition, 2019.
D. Sakkos, H. Shum, E. Ho, "Illumination-Based Data Augmentation for Robust Background Subtraction", International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019, Ulkulhas, Maldives, 2019.
D. Sakkos, "Video Foreground Segmentation with Deep Learning", PhD Thesis, Northumbria University, May 2020.
Y. Tao, Z. Ling, I. Patras, "Universal Foreground Segmentation Based on Deep Feature Fusion Network for Multi-Scene Videos", IEEE Access, Volume 7, pages 158326-158337, 2019.
C. Barla, “Learning Fully Convolutional Networks for Background Subtraction In Surveillance Videos”, PhD Thesis, San Diego State University, USA, 2019.
J. Jung, J. Jang, J. Hong, "Cosine Focal Loss-based Change Detection for Video Surveillance Systems", IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019, Taipei, Taiwan, 2019.
D. Santos, R. Pires, D. Colombo, J. Papa, "Video Segmentation Learning Using Cascade Residual Convolutional Neural Network", SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2019, pages 1-7, Rio de Janeiro, Brazil, 2019.
A. Shahbaz, V. Hoang, K. Jo, "Convolutional Neural Network based Foreground Segmentation for Video Surveillance Systems", Annual Conference of the IEEE Industrial Electronics Society, IECON 2019, Lisbon, Portugal, 2019.
C. Herrera, F. Krach, J. Teichmann, “Denise: Deep Learning based Robust PCA for Positive Semi definite Matrices”, preprint, April 2020. [RPCA]
J. Yang, W. Shi, K. Li, X. Song, H. Yue, "Fusing Spatiotemporal Clues with Cascading Neural Networks for Foreground-Background Separation", Journal of Tianjin University, Volume 53, No. 6, June 2020.
G. Kumar, B. Sowjanya, "Foreground Segmentation asbed On Convolutional Neural Network", Mukt Shabd Journal, 2020.
S. Li, P. Han, S. Bu, P. Tong, Q. Li, K. Li, G. Wan, "Change detection in images using shape-aware siamese convolutional network", Engineering Applications of Artificial Intelligence, 2020.
M. Vijayan, R. Mohan, "A Universal Foreground Segmentation Technique using Deep-Neural Network", Multimedia Tools and Applications, May 2020.
M. Mandal, S. Vipparthi, "Scene Independency Matters: An Empirical Study of Scene Dependent and Scene Independent Evaluation for CNN-Based Change Detection", IEEE Transactions on Intelligent Transportation Systems, 2020.
J. Zhang, S. Wang, J. Qiu, X. Pan, J. Zou, Y. Duan, Z. Pan, Y. Li, "A fast X-shaped foreground segmentation network with CompactASPP", Engineering Applications of Artificial Intelligence, Volume 97, January 2021.
I. Osman, M. Shehata, "MODSiam: Moving Object Detection using Siamese Networks", IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2020, pages 1-6, Canada, 2020.
I. Osman, M. Shehata, "Few-Shot Learning Network for Moving Object Detection using Exemplar-Based Attention Map", IEEE International Conference on Image Processing, ICIP 2022, pages 1056-1060, 2022.
M. Mandal, V. Dhar, A. Mishra, S. Vipparthi, M. Abdel-Mottaleb, "3DCD: Scene Independent End-to-End Spatiotemporal Feature Learning Framework for Change Detection in Unseen Videos", IEEE Transactions on Image Processing, Volume 30, pages 546-558, 2021.
A. Shahbaz, K. Jo, "Dual Camera-based Supervised Foreground Detection for Low-end Video Surveillance Systems", IEEE Sensors Journal, 2021.
M. Vijayan, P. Raguraman, R. Mohan, "A Fully Residual Con-olutional Neural Network for Background Subtraction", Pattern Recognition Letters, 2021.
R. Rabidas, D. Ravi, S. Pradhan, R. Moudgollya, A. Ganguly, "Investigation and Improvement of VGG based Encoder-Decoder Architecture for Background Subtraction", Advanced Communication Technologies and Signal Processing, ACTS 2020, 2020.
D Liang, Z Wei, H Sun, H. Zhou, "Robust Cross-Scene Foreground Segmentation in Surveillance Video", IEEE International Conference on Multimedia and Expo, ICME 2021, July 2021.
S. Qu, H. Zhang, W. Wu, W. Xu, Y. Li, “Symmetric pyramid attention convolutional neural network for moving object detection”, Signal, Image and Video Processing, 2021.
J. Morais, A. Fernandes, A. Ferreira, B. Faria, "Performance Analysis of a Foreground Segmentation Neural Network Model", Preprint, May 2021.
Y. Yang, J. Ruan, Y. Zhang, X. Cheng, Z. Zhang, G. Xie, "STPNet: A Spatial-Temporal Propagation Network for Background Subtraction", IEEE Transactions on Circuits and Systems for Video Technology, June 2021.
Z. Zou, Z. Meng, L. Shu, J. Hao, "A Change-Aware Approach for Relative Motion Segmentation", IEEE International Conference on Multimedia and Expo, ICME 2021, pages 1-6, 2021.
R. Liu, Y. Ruichek, M. El Bagdouri, "Multispectral Background Subtraction with Deep Learning", Journal of Visual Communication and Image Representation, Volume 80, October 2021.
H. Fu, Z. Ma, B. Zhao, Z. Yang, Y. Jiang, M. Zhu, "Lightweight Convolutional Neural Network for Foreground Segmentation", Chinese Intelligent Systems Conference, pages 811-819, 2021.
J. Zhang, X. Zhang, Y. Zhang, Y. Duan, Y. Li, Z. Pan, "Meta-knowledge Learning and Domain Adaptation for Unseen Background Subtraction", IEEE Transactions on Image Processing, October 2021.
B. Hou, Y. Liu, N. Ling, L. Liu, Y. Ren, "A Fast Lightweight 3D Separable Convolutional Neural Network With Multi-Input Multi-Output for Moving Object Detection", IEEE Access, 2021.
B. Hou, Y. Liu, N. Ling, L. Liu, Y. Ren, M. Hsu, "F3DsCNN: A Fast Two-Branch 3D Separable CNN for Moving Object Detection", IEEE International Conference on Visual Communications and Image Processing, VCIP 2021, pages 1-5, 2021.
Y. Suzuki, K. Ichige, "High Accuracy Video Foreground Segmentation based on Feature Normalization", IEEE International Symposium on Communications and Information Technologies, ISCIT 2021, pages 15-17, 2021.
H. Zhang, S. Qu, H. Li, "Dual-Branch Enhanced Network for Change Detection", Arabian Journal for Science and Engineering, 2021.
J. Zhang, Y. Li, C. Ren, L. Huang, “Cross-Scene Foreground Segmentation Algorithm based on High-Level Feature Differencing Between Frames”, Acta Electronica Sinica, October 2021.
M. El Rai, M. Al-Saad, M. Darweesh, S. Al-Mansoori, H. Al-Ahmad, W. Mansoor, "Moving Objects Segmentation in Infrared Scene Videos", IEEE International Conference on Signal Processing and Information Security, ICSPIS 2021, 2021.
C. Xu, H. Liu, T. Li, Y. Zhang, T. Li, G. Li, “Cascaded Feature-Mask Fusion for Foreground Segmentation”, IEEE Open Journal of Intelligent Transportation Systems, 2022.
A. Sharma, V. Bajpai, B. Subudhi, T. Veerakumar, V. Jakhetiya, "Encoder and Decoder Network with ResNet-50 and Global Average Feature Pooling for Local Change Detection", Computer Vision and Image Understanding, September 2022.
G. Balachandran, J. Krishnan, “Moving scene-based video segmentation using fast convolutional neural network integration of VGG-16 net deep learning architecture”, International Journal of Modeling, Simulation, and Scientific Computing, May 2022.
K. Chan, J. Wang, H. Yu, "A combination of background modeler and encoder-decoder CNN for background/foreground segregation in image sequence", Signal, Image and Video Processing, 2022.
M. Panda, B. Subudhi, T. Bouwmans, V. Jakhetiya, T. Veerakumar, “An End to End Encoder-Decoder Network with Multi-scale Feature Pulling for Detecting Local Changes From Video Scene”, IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2022, Madrid, Spain, November 2022.
H. Phan, S. Ha, P. Ha, "Towards Communication-Efficient Distributed Background Subtraction", Asian Conference on Intelligent Information and Database Systems, ACIIDS 2022, pages 490-502, November 2022.
Z. Kuang, X. Tie, X. Wu, L. Ying, "FUNet: Flow Based Conference Video Background Subtraction", International Conference on Smart Multimedia, ICSM 2022, pages 18-28, December 2022.
T. Nandhini, K. Thinakaran, "CNN Based Moving Object Detection from Surveillance Video in Comparison with GMM", International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2022, Chennai, India, pages 1-6, 2022.
G. Dong, C. Zhao, X. Pan, A. Basu, "Learning Temporal Distribution and Spatial Correlation for Universal Moving Object Segmentation", Preprint, April 2023.
G. Dong, C. Zhao, X. Pan, A. Basu, “Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentation”, IEEE Transactions on Image Processing, 2024.
R. Jiang, R. Zhu, X. Cai, H. Su, "Foreground Segmentation Network with Enhanced Attention", Journal of Shanghai Jiaotong University, April 2023.
G. Rahmon, K. Palaniappan, I. Toubal, F. Bunyak, R. Rao, G. Seetharaman, "DeepFTSG: Multi-stream Asymmetric USE-Net Trellis Encoders with Shared Decoder Feature Fusion Architecture for Video Motion Segmentation", International Journal of Computer Vision, October 2023.
W. Kim, K. Lee, S. Woo, M. Cho, S. Lee, "RMOSNet: A Robust Moving Object Segmentation Network with Adaptive Background Modeling", October 2023.
T. Sahoo, B. Mohanty, B. Pattanayak, “Moving Object Detection using Deep Learning” Method. International Journal of Intelligent Systems and Applications in Engineering, Volume 12, pages 282-290, 2023.
P. Sahoo, U. Panigrahi, M. Panda, “A Resnet-101 Deep Learning Framework Induced Transfer Learning Strategy for Local Change Detection”, International Journal of Computer Vision, October 2023.
M. Panda, B. Subudhi, T. Veerakumar, V. Jakhetiya, "Modified ResNet-152 Network with Hybrid Pyramidal Pooling for Local Change Detection", IEEE Transactions on Artificial Intelligence, October 2023.
T. Ruan, S. Wei, Y. Zhao, B. Guo, Z. Yu, “LO2net: Global–Local Semantics Coupled Network for scene-specific video foreground extraction with less supervision”, Pattern Analysis and Applications, 2023.
A. Ganivada, S. Yara, “A novel deep convolutional encoder–decoder network: application to moving object detection in videos”, Neural Computing and Applications, Volume 35, pages 22027-22041, September 2023.
K. Chan, J. Wang, H. Yu, “A combination of background modeler and encoder-decoder CNN for background/foreground segregation in image sequence”, Signal, Image and Video Processing, Volume 17, pages 1297-1304, 2023.
A. Turker, E. Eksioglu, "A fully convolutional encoder-decoder network for moving object segmentation", International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022, pages1-6, 2022.
A. Turker, E. Eksioglu, “3D convolutional long short-term encoder-decoder network for moving object segmentation”, Computer Science and Information Systems, 2023.
P. Wang, J. Wu, A. Fang, Z. Zhu, C. Wang, S. Ren, "Fusion representation learning for foreground moving object detection", Digital Signal Processing, June 2023.
P. Wang, J. Wu, A. Fang, Z. Zhu, C. Wang, P. Mu, "Contrastive fusion representation learning for foreground object detection", Engineering Applications of Artificial Intelligence, 2023.
X. Tian, P. Zheng, J. Huang, "Secure Deep Learning Framework for Moving Object Detection in Compressed Video”, IEEE Transactions on Dependable and Secure Computing, 2023.
V. Gowda, M. Gopalakrishna, J. Megha, S. Mohankumar, “Foreground segmentation network using transposed convolutional neural networks and up sampling for multiscale feature encoding”, Neural Networks, Volume 170, pages 167-175, February 2024.
N. Chung, S. Ha, "BgSubNet: Robust Semi-Supervised Background Subtraction In Realistic Scenes", IEEE Sensors Journal, February 2024.
P. Pokala, J. Patibandla, N. Pandey, B. Pailla, “MUSTAN: Multi-scale Temporal Context as Attention for Robust Video Foreground Segmentation”, Preprint, February 2024.
A. Turker, E. Eksioglu, "3D Convolutional Long Short-Term Encoder-Decoder Network for Moving Object Segmentation", Computer Science and Information Systems, Volume 21, Issue 1, pages 363-378, January 2024.
U. Panigrahi, P. Sahoo, M. Panda, S. Parija, S. Dash, S. Mallik, H. Qin, “An Improved VGG-16 Deep Learning Framework Induced Feature Extraction Framework for Moving Object Detection”, SSRN Preprint, April 2024.
U. Panigrahi, P. Sahoo, M. Panda, G. Panda, “A ResNet-101 Deep Learning Framework Induced Transfer Learning Strategy for Moving Object Detection”, Image and Vision Computing, Volume 146, June 2024.
U-Net (21 papers)
R. Bolano, M. Serra, P. Puig, "Foreground Detection in a Multi-Target Fish Tracking from Video-Recordings using U-Net based Architecture"; Frontiers in Artificial Intelligence and Applications, IOS Press, pages 381-385, 2018.
M. Tezcan, J. Konrad, P. Ishwar, "A Fully-Convolutional Neural Network for Background Subtraction of Unseen Videos", Preprint, July 2019.
M. Tezcan, J. Konrad, P. Ishwar, "BSUV-Net: A Fully-Convolutional Neural Network for Background Subtraction of Unseen Video", IEEE WACV 2020, pages 2763-2772, 2020.
M. Tezcan, P. Ishwar, J. Konrad, "BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction", Preprint, 2021.
M. Tezcan, "Deep learning algorithms for background subtraction and people detection", PhD Thesis, Boston University, USA, 2021.
V. Mondéjar-Guerra, J. Rouco, J. Novo, M. Ortega, "An end-to-end deep learning approach for simultaneous background modeling and subtraction", British Machine Vision Conference, September 2019.
V. Mondéjar-Guerra, L. Ramos, J. Rouco, J. Novo, M. Ortega,"Background/Foreground Classification using Two Nested Networks", IV Machine Learning Workshop Galicia, October 2019.
M. Santana, L. Passos, T. Moreira, D. Colombo, V. De Albuquerque, J. Papa, "A Novel Siamese-based Approach for Scene Change Detection with Applications to Obstructed Routes in Hazardous Environments", Intelligent Systems, 2019.
J. Kim, J. Ha, "Foreground Objects Detection using a Fully Convolutional Network with a Background Model Image and Multiple Original Images", IEEE Access, 2020.
J. Kim, J. Ha, "Spatio-temporal Data Augmentation for Visual Surveillance", Preprint, 2021.
J. Kim, J. Ha, "Foreground Objects Detection by U-Net with Multiple Difference Images", MDPI Applied Sciences, 2021.
G. Rahmon, F. Bunyak, G. Seetharaman, K. Palaniappan, "Motion U-Net: Multi-cue Encoder-Decoder Network for Motion Segmentation", International Conference on Pattern Recognition, ICPR 2021, pages 8125-8132, 2021.
R. Kalsotra, S. Arora, "Performance analysis of U-Net with hybrid loss for foreground detection", Multimedia Systems, 2022.
K. Takeda, K. Fujiwara, T. Sakai, "Unsupervised Deep Learning for Online Foreground Segmentation Exploiting Low-Rank and Sparse Priors", IEEE International Conference on Digital Image Computing: Techniques and Applications, DICTA 2022, Sydney, Australia, 2022.
Y. Li, "Detection of Moving Object using Super-Pixel Fusion Network", ACM Transactions on Multimedia Computing, Communications, and Applications, January 2023.
F. Bahri, N. Ray, "Weakly Supervised Real-Time Dynamic Background Subtraction", Preprint, March 2023.
F. Gouizi, A. Megherbi, "Nested-Net: A Deep Nested Network for Background Subtraction", International Journal of Multimedia Information Retrieval, March 2023.
V. Bajpai, A. Sharma, B. Subudhi, T. Veerakumar, V. Jakhetiya, "Underwater U-Net: Deep Learning with U-Net for Visual Underwater Moving Object Detection", OCEANS 2021, San Diego, USA, pages 1-4, September 2021.
M. Kapoor, S. Patra, B. Subudhi, V. Jakhetiya, A. Bansal, "Underwater Moving Object Detection Using an End-to-End Encoder-Decoder Architecture and GraphSage With Aggregator and Refactoring", WiCV Workshop in conjunction with IEEE Conference on Computer Vision and Pattern Recognition, CVPRW 2023, Vancouver, Canada, 2023.
K. Takeda, K. Fujiwara, T. Sakai, “Unsupervised deep learning for online foreground segmentation exploiting low-rank and sparse priors”, International Conference on Digital Image Computing: Techniques and Applications, DICTA 2022, pages 1-7, Sydney, Australia, 2022. [RPCA]
K. Takeda, T. Sakai, “Unsupervised deep learning of foreground objects from low-rank and sparse dataset”, Computer Vision and Image Understanding, 2024. [RPCA]
Convolutional Density Approximation (1)
S. Ha, T. Nguyen, H. Phan, P. Ha, “Real-Time Change Detection with Convolutional Density Approximation”, Vietnam Journal of Computer Science, 2023. [MOG]
Deep Variation Transformation Neural Network (1 paper)
Y. Ge, J. Zhang, X. Ren, C. Zhao, J. Yang, A. Basu, "Deep Variation Transformation Network for Foreground Detection", IEEE Transactions on Circuits and Systems for Video Technology, 2020.
Fluid Pyramid Integration Networks (1 paper)
R. Huang, M. Zhou, Y. Xing, Y. Zou, W. Fan, "Change detection with various combinations of fluid pyramid integration networks", Neurocomputing, 2021.
Arithmetic Distribution Neural Networks (3 papers)
C. Zhao, K. Hu, A. Basu, "Arithmetic Distribution Neural Network for Background Subtraction", Preprint, 2021.
C. Zhao, K. Hu, A. Basu, "Universal Background Subtraction based on Arithmetic Distribution Neural Network", IEEE Transactions on Image Processing, Volume 31, pages 2934-2949, 2022.
P. Batra, G. Singh, N. Goyal, "Application Of ADNN For Background Subtraction In Smart Surveillance System", Preprint, December 2022.
Transformer Neural Networks (3 papers)
I. Osman, M. Abdelpakey, M. Shehata, "TransBlast: Self-Supervised Learning using Augmented Subspace with Transformer for Background/Foreground Separation", Fourth Workshop on Robust Subspace Learning and Computer Vision, RSL-CV 2021 in conjunction with ICCV 2021, October 2021.
Z. Wang, Y. Zhang, L. Luo, N. Wang, "TransCD: scene change detection via transformer-based architecture", Optics Express, Volume 29, Issue 25, pages 41409-41427, 2021.
H. Cui, Z. Lv, T. Yuan, C. Feng, X. Shan, "GIBS-Net: Unseen Video Background Subtraction with Global Information", IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023, pages 125-130, Chongqing, China, 2023.
Graph Convolutional Neural Networks (2 papers)
J. Giraldo, S. Javed, N. Werghi, T. Bouwmans, "Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos ", Fourth Workshop on Robust Subspace Learning and Computer Vision, RSL-CV 2021 in conjunction with ICCV 2021, October 2021.
W. Prummel, J. Giraldo, A. Zakharova, T. Bouwmans, "Inductive Graph Neural Networks for Moving Object Segmentation", IEEE International Conference on Image Processing, ICIP 2023, Kuala Lumpur, Malaysia, October 2023.
Graph Neural Network (1 paper)
C. Zeng, Y. Qiao, "A Moving Object Detection Method Based on Graph Neural Network”, International Conference on Computer Vision, Image and Deep Learning, CVIDL 2023, pages 549-554, Zhuhai, China, 2023.
Generative Adversarial Network (GAN) (27 papers)
M. Sultana, A. Mahmood, S. Javed, S. Jung, “Unsupervised Deep Context Prediction for Background Estimation and Foreground Segmentation”, Machine Vision and Applications, October 2018.
M. Sultana, A. Mahmood, S. Javed, S. Jung, "Unsupervised RGBD Video Object Segmentation using GANs", ACCV-Workshops 2018, December 2018.
M. Sultana, S. Jung, "Illumination Invariant Foreground Object Segmentation using ForeGANs", Preprint, February 2019.
M. Sultana, S. Jung, "Illumination Invariant Foreground Object Segmentation using ForeGANs", International Workshop on Frontiers of Computer Vision, IW-FCV 2019, 2019.
M. Sultana, A. Mahmood, T. Bouwmans, S. Jung, "Complete Moving Object Detection in the Context of Robust Subspace Learning", Third Workshop on Robust Subspace Learning and Computer Vision, ICCV 2019, Seoul, South Korea, October 2019.
M. Sultana, A. Mahmood, T. Bouwmans, S. Jung, "Unsupervised Adversarial Learning for Dynamic Background Modeling", International Workshop on Frontiers of Computer Vision, IW-FCV 2020, Ibusuki, Japan, February 2020 .
M. Sultana, A. Mahmood, T. Bouwmans, S. Jung, "Dynamic Background Subtraction Using Least Square Adversarial Learning", IEEE International Conference on Image Processing, ICIP 2020, Abu Dhabi, UAE, October 2020.
M. Sultana, A. Mahmood, S. Jung, "Unsupervised Moving Object Detection in Complex Scenes Using Adversarial Regularizations", IEEE Transactions on Multimedia, 2020.
M. Sultana, A. Mahmood, T. Bouwmans, M. Khan, S. Jung, "Background/Foreground Separation: Guided Attention based Adversarial Modeling (GAAM) versus Robust Subspace Learning Methods", Fourth Workshop on Robust Subspace Learning and Computer Vision, RSL-CV 2021 in conjunction with ICCV 2021, Octbober 2021.
M. Sultana, A. Mahmood, S. Jung, “Unsupervised Moving Object Segmentation using Background Subtraction and Optimal Adversarial Noise Sample Search”, Pattern Recognition, 2022.
M. Bakkay, H. Rashwan, H. Salmane, L. Khoudoury D. Puig, Y. Ruichek, "BSCGAN: Deep Background Subtraction with Conditional Generative Adversarial Networks", IEEE International Conference on Image Processing, ICIP 2018, Athens, Greece, October 2018.
W. Zheng, K. Wang, F. Wang, "Background Subtraction Algorithm based on Bayesian Generative Adversarial Networks", Acta Automatica Sinica, 2018.
W. Zheng, K. Wang, F. Wang, "A Novel Background Subtraction Algorithm based on Parallel Vision and Bayesian GANs", Neurocomputing, 2018.
F. Bahri, M. Shakeri, N. Ray, "Online Illumination Invariant Moving Object Detection by Generative Neural Network", Preprint, 2018. [RPCA]
D. Sakkos, E. Ho, H. Shum, "Illumination-aware Multi-task GANs for Foreground Segmentation", IEEE Access, 2018.
P. Patil, S. Murala, "FgGAN: A Cascaded Unpaired Learning for Background Estimation and Foreground Segmentation", IEEE Winter Conference on Applications of Computer Vision, WACV 2019, pages 1770-1778, 2019.
P. Patil, O. Thawakar, A. Dudhane, S. Murala, "Motion Saliency Based Generative Adversarial Network for Underwater Moving Object Segmentation", IEEE International Conference on Image Processing, ICIP 2019, pages 1565-1569, Taipei, Taiwan, 2019.
P. Patil, A. Dudhane, S.Murala, "Multi-Frame Recurrent Adversarial Network for Moving Object Segmentation", IEEE Winter Conference on Applications of Computer Vision, WACV 2021, January 2021.
W. Zheng, K. Wang, F. Wang, "A novel background subtraction algorithm based on parallel vision and Bayesian GANs", Neurocomputing, 2019.
Z. Zhu, Y. Meng, D. Kong, X. Zhang, Y. Guo, Y. Zhao, "To See in the Dark: N2DGAN for Background Modeling in Nighttime Scene", Preprint, 2019.
H. Didwania, S. Ghatak, S. Rup, "Multi-frame and Multi-scale Conditional Generative Adversarial Networks for Efficient Foreground Extraction", International Conference on Computer Vision and Image Process, CVIP 2019, pages 211-222, 2019.
S. Ghatak, S. Rup, H. Didwania, M. Swamy , “GAN based efficient foreground extraction and HGWOSA based optimization for video synopsis generation”, Digital Signal Processing, Volume 111, April 2021.
W. Yu, J. Bai, L. Jiao, "Background Subtraction Based on GAN and Domain Adaptation for VHR Optical Remote Sensing Videos", IEEE Access, 2020.
F. Bahri, N. Ray, "Dynamic Background Subtraction by Generative Neural Networks", Preprint, February 2022.
F. Bahri, N. Ray, "Dynamic Background Subtraction by Generative Neural Networks", IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2022, Madrid, Spain, November 2022.
I. Kajo, M. Kas, Y. Ruichek, N. Kamel, "Tensor based Completion Meets Adversarial Learning: a Win-Win Solution for Change Detection on Unseen Videos", Computer Vision and Image Understanding, 2022.
F. Bahri, “Moving Object Detection Using Unsupervised and Weakly Supervised Neural Networks in Videos with Illumination Changes and Dynamic Background”, PhD Thesis, Department of Computing Science, University of Alberta, 2023.
Deep Adversarial Networks (1 paper)
P. Patil, A. Dudhane, S. Murala, A. Gonde, "Deep Adversarial Network for Scene Independent Moving Object Segmentation", IEEE Signal Processing Letters, Volume 28, pages 489-493, 2021.