Deep Auto-encoder Networks (DAN) (9 papers)
Y. Zhang, X. Li, Z. Zhang, F. Wu, L. Zhao, “Deep Learning Driven Blockwise Moving Object Detection with Binary Scene Modeling”, Neurocomputing, June 2015.
J. Garcia-Gonzalez, J. Ortiz-de-Lazcano-Lobato, R. Luque-Baena, M. Molina-Cabello, "Background Modeling for Video Sequences by Stacked Denoising Autoencoders", Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2018 , pages 341-350, September 2018.
J. Garcia-Gonzalez, J. Ortiz-de-Lazcano-Lobato, R. Luque-Baena, E. Lopez-Rubio, "Background Modeling by Shifted Tilings of Stacked Denoising Autoencoders",International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, pages 307-316, May 2019.
J. Garcia-Gonzalez, J. Ortiz-de-Lazcano-Lobato, R. Luque-Baena, M. Molina-Cabello, E. Lopez-Rubio, "Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences", Pattern Recognition Letters, 2019.
J. Garcia-Gonzalez, J. Ortiz-de-Lazcano-Lobato, R. Luque-Baena, E. Lopez-Rubio, “Background subtraction by probabilistic modeling of patch features learned by deep autoencoders”, Integrated Computer-Aided Engineering, 2020.
J. Garcia-Gonzalez, M. Molina-Cabello, R. Luque-Baena, J. Ortiz-de-Lazcano-Lobato, E. Lopez-Rubo, "Deep Autoencoder Architectures for Foreground Object Detection in Video Sequences Based on Probabilistic Mixture Models", IEEE International Conference on Image Processing, ICIP 2020, Abu Dhabi, UAE, October 2020.
J. Garcia-Gonzalez, J. Ortiz-de-Lazcano-Lobato, R. Luque-Baena, E. Lopez-Rubio, "Foreground detection by probabilistic mixture models using semantic information from deep networks", European Conference on Artificial Intelligence ,ECAI 2020, 2020.
U. Panigrahi, M. Panda, P. Kumar, S. Parija, D. Rout, A. Samantaray, "Moving Object Detection for Thermal Video using Encoder-Decoder Type Deep Learning Framework", Parul International Conference on Engineering and Technology, PICET 2024, Vadodara, India, 2024.
H. Zhang, L. Yang, "A Moving Object Detection Method Based on Conditional Information and Feature Deepening", Journal of Electrical and Computer Engineering, 2025.
Neural Reponse Mixture (NeREM) (2 papers)
M. Shafiee, P. Siva, P. Fieguth, A. Wong, “Embedded Motion Detection via Neural Response Mixture Background Modeling”, International Conference on Computer Vision and Pattern Recognition, CVPR 2016, June 2016.
M. Shafiee, P. Siva, P. Fieguth, A. Wong, “Real-Time Embedded Motion Detection via Neural Response Mixture Modeling”, Journal of Signal Processing Systems, June 2017.
Convolutional Neural Networks (CNN) (9 papers)
T. Nguyen, C. Pham, S. Ha, J. Jeon, "Change Detection by Training a Triplet Network for Motion Feature Extraction", IEEE Transactions on Circuits and Systems for Video Technology, January 2018.
J. Dou, Q. Qin, Z. Tu,"Background subtraction based on deep convolutional neural networks features", Multimedia Tools and Applications, pages 1-23, 2018.
J. Dou, Q. Qin, Z. Tu, "Deep Convolutional Neural Networks Features For Robust Foreground Segmentation", Chinese Control And Decision Conference, CCDC 2019, pages 3576-3581, Nanchang, China, 2019.
M. Mandal, V. Dhar, A. Mishra, S. Vipparthi, "3DFR: A Swift 3D Feature Reductionist Framework for Scene Independent Change Detection", IEEE Signal Processing Letters, 2019.
A. Shahbaz, K. Jo, "Moving Object Detection based on Deep Atrous Spatial Features for Moving Camera", IEEE International Symposium on Industrial Electronics, ISIE 2020, 2020.
A. Shahbaz, K. Jo, "Deep Atrous Spatial Features based Supervised Foreground Detection Algorithm for Industrial Surveillance Systems", IEEE Transactions on Industrial Informatics, 2020.
H. Zhang, H. Li, "Interactive spatio-temporal feature learning network for video foreground detection", Complex and Intelligent Systems, March 2022.
Y. Lu, L. Chen, X. Wang, X. Xu, X. Sun “Foreground Detection Through Feature Fusion Convolutional Neural Networks: Enhancing Robustness Against Complex Backgrounds”, The Visual Computer, May 2025.
Restricted Boltzmann Machines (1 paper)
S. Lee, D. Kim, "Background Subtraction using the Factored 3-Way Restricted Boltzmann Machines", Preprint, 2018.