Communications in Computer and Information Science
Moving Object Segmentation (MOS) is an important topic in computer vision. MOS becomes a challeng... more Moving Object Segmentation (MOS) is an important topic in computer vision. MOS becomes a challenging problem in the presence of dynamic background and moving camera videos such as Pan-Tilt-Zoom cameras (PTZ). The MOS problem has been solved using unsupervised and supervised learning strategies. Recently, new ideas to solve MOS using semi-supervised learning have emerged inspired from the theory of Graph Signal Processing (GSP). These new algorithms are usually composed of several steps including: segmentation, background initial-ization, features extraction, graph construction, graph signal sampling, and a semi-supervised learning algorithm inspired from reconstruction of graph signals. In this work, we summarize and explain the theoretical foundations as well as the technical details of MOS using GPS. We also propose two architectures for MOS using semi-supervised learning and a new evaluation procedure for GSP-based MOS algorithms. GSP-based algorithms are evaluated in the Change Detection (CDNet2014) dataset for MOS, outperforming numerous State-Of-The-Art (SOTA) methods in several challenging conditions.
Conventional neural networks show a powerful framework for background subtraction in video acquir... more Conventional neural networks show a powerful framework for background subtraction in video acquired by static cameras. Indeed, the well-known SOBS method and its variants based on neural networks were the leader methods on the largescale CDnet 2012 dataset during a long time. Recently, convolutional neural networks which belong to deep learning methods were employed with success for background initialization, foreground detection and deep learned features. Currently, the top current background subtraction methods in CDnet 2014 are based on deep neural networks with a large gap of performance in comparison on the conventional unsupervised approaches based on multi-features or multi-cues strategies. Furthermore, a huge amount of papers was published since 2016 when Braham and Van Droogenbroeck published their first work on CNN applied to background subtraction providing a regular gain of performance. In this context, we provide the first review of deep neural network concepts in background subtraction for novices and experts in order to analyze this success and to provide further directions. For this, we first surveyed the methods used background initialization, background subtraction and deep learned features. Then, we discuss the adequacy of deep neural networks for background subtraction. Finally, experimental results are presented on the CDnet 2014 dataset.
Communications in Computer and Information Science
Moving Object Segmentation (MOS) is an important topic in computer vision. MOS becomes a challeng... more Moving Object Segmentation (MOS) is an important topic in computer vision. MOS becomes a challenging problem in the presence of dynamic background and moving camera videos such as Pan-Tilt-Zoom cameras (PTZ). The MOS problem has been solved using unsupervised and supervised learning strategies. Recently, new ideas to solve MOS using semi-supervised learning have emerged inspired from the theory of Graph Signal Processing (GSP). These new algorithms are usually composed of several steps including: segmentation, background initial-ization, features extraction, graph construction, graph signal sampling, and a semi-supervised learning algorithm inspired from reconstruction of graph signals. In this work, we summarize and explain the theoretical foundations as well as the technical details of MOS using GPS. We also propose two architectures for MOS using semi-supervised learning and a new evaluation procedure for GSP-based MOS algorithms. GSP-based algorithms are evaluated in the Change Detection (CDNet2014) dataset for MOS, outperforming numerous State-Of-The-Art (SOTA) methods in several challenging conditions.
Conventional neural networks show a powerful framework for background subtraction in video acquir... more Conventional neural networks show a powerful framework for background subtraction in video acquired by static cameras. Indeed, the well-known SOBS method and its variants based on neural networks were the leader methods on the largescale CDnet 2012 dataset during a long time. Recently, convolutional neural networks which belong to deep learning methods were employed with success for background initialization, foreground detection and deep learned features. Currently, the top current background subtraction methods in CDnet 2014 are based on deep neural networks with a large gap of performance in comparison on the conventional unsupervised approaches based on multi-features or multi-cues strategies. Furthermore, a huge amount of papers was published since 2016 when Braham and Van Droogenbroeck published their first work on CNN applied to background subtraction providing a regular gain of performance. In this context, we provide the first review of deep neural network concepts in background subtraction for novices and experts in order to analyze this success and to provide further directions. For this, we first surveyed the methods used background initialization, background subtraction and deep learned features. Then, we discuss the adequacy of deep neural networks for background subtraction. Finally, experimental results are presented on the CDnet 2014 dataset.
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Papers by Maryam Sultana