Neural Networks

1. Traditionnal Neural Networks (9 papers)

D. Culibrk, O.Marques, D. Socek, H. Kalva, B. Furht, "Neural network approach to background modeling for video object segmentation" IEEE Transactions on Neural Netwoks,  Volume 18, Issue 1, pages 614–162, 2007.

R. Luque, J. Ortiz-De-Lazcano-Lobato, E. Lopez-Rubio, E. Palomo, "Object tracking in video sequences by unsupervised learning", International Conference on Computer Analysis of Images and Patterns, CAIP 2009, 2009.

E. Lopez-Rubio, R. Baena, E. Domınguez, "Foreground detection in video sequences with probabilistic self-organizing maps", International Journal  of Neural Systems, Volume 21, pages 225–246, 2011.

L. Maddalena, A. Petrosino, “The SOBS Algorithm: What Are the Limits?”, IEEE Workshop on Change Detection, CVPR 2012, June 2012.

J. Ramirez-Quintana, M. Chacon-Murguia, “Self-adaptive SOM-CNN neural system for dynamic object detection in normal and complex scenarios”, Pattern Recognition, September 2014.

M. De Gregorio, M. Giordano, “Background Estimation by Weightless Neural Networks", Pattern Recognition Letters, December 2016.

Z.  Zhao, X. Zhang, Y. Fang, “Stacked Multi-layer Self-Organizing Map for Background Modeling”, IEEE Transactions on Image Processing, 2015.

G. Gemignani, A. Rozza, “A Novel Background Subtraction Approach based on Multi-layered Self-Organizing Maps”, IEEE International Conference on Image Processing, September 2015.

2. Deep Learning Neural Networks (17 papers)

2.1 Restricted Boltzman Machine (3 papers)

R. Guo, H. Qi, "Partially-Sparse Restricted Boltzmann Machine for Background Modeling and Subtraction", International Conference on Machine Learning and Applications, ICMLA 2013, pages 209-214, December 2013.

L. Xu, Y. Li, Y. Wang, E. Chen, "Temporally Adaptive Restricted Boltzmann Machine for Background Modeling", American Association for Artificial Intelligence, AAAI 2015, January 2015.

A. Sheri, M. Rafique, M. Jeon, W. Pedrycz, “Background subtraction using Gaussian Bernoulli restricted Boltzmann machine”, IET Image Processing, 2018.

2.2  Deep Auto-encoder Networks (4 papers)

P. Xu, M. Ye, X. Li, Q. Liu, Y. Yang, J. Ding, “Dynamic Background Learning through Deep Auto-encoder Networks”, ACM International Conference on Multimedia, Orlando, FL, USA, November 2014.

P. Xu, M. Ye, Q. Liu, X. Li, L. Pei, J. Ding, “Motion Detection via a Couple of Auto-Encoder Networks”, International Conference on Multimedia and Expo, ICME 2014, 2014.

Z. Qu, S. Yu, M. Fu, "Motion Background Modeling based on Context-encoder", IEEE International Conference on Artificial Intelligence and Pattern Recognition, ICAIPR 2016, September 2016.

B. Rezaei, A. Farnoosh, S. Ostadabbas, "G-LBM : Generative Low-dimensional Background Model Estimation from Video Sequences", Preprint, March 2020.

2.3 Convolutional Neural Networks (4)

I. Halfaoui, F. Bouzaraa, O. Urfalioglu, "CNN-Based Initial Background Estimation", Scene Background Modeling Contest in conjunction with ICPR 2016, 2016.

Y. Tao, P. Palasek, Z. Ling, I. Patras, "Background modelling based on generative Unet", IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017, September 2017.

W. J. Kim, S. Hwang, J. Lee, S. Woo, S. Lee, "AIBM: Accurate and Instant Background Modeling for Moving Object Detection",  IEEE Transactions on Intelligent Transportation Systems, 2021.

M. Esfahani, A. Jamadi,  M. Esfaha, "ISAIR: Deep Inpainted Semantic Aware Image Representation for Background Subtraction", Expert Systems with Applications, December 2021.

2.4  Generative Adversarial Networks (6 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, "Unsupervised Adversarial Learning for Dynamic Background Modeling", International Workshop on Frontiers of Computer Vision, IW-FCV 2020, Ibusuki, Japan, February 2020.

J. Kim, J. Ha, "Visual Surveillance using Background Model Image Generated by GAN”, IEEE International Conference on Control, Automation and Systems, ICCAS 2020,  pages 292-295, Busan, South Korea, 2020.