Thierry Bouwmans

Thierry Bouwmans
La Rochelle Université · Laboratoire MIA (Mathématiques, Image et Applications)

Associate Professor

About

155
Publications
86,904
Reads
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6,905
Citations
Citations since 2016
91 Research Items
5794 Citations
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201620172018201920202021202202004006008001,000
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Introduction
Thierry Bouwmans is an Associate Professor at the University of La Rochelle, France. Since 2006, his research interests consist mainly in the detection of moving objects in challenging environments as it is a pre-step for behaviour detection in video surveillance. He has authored more than 50 papers in refereed international journals and conferences in the field of background modeling and foreground detection, and has co-edited two books in CRC Press (background/foreground separation for video surveillance, robust PCA via decomposition in low rank and sparse matrices).
Additional affiliations
February 2001 - present

Publications

Publications (155)
Preprint
Full-text available
Semantic segmentation is a fundamental topic in computer vision. Several deep learning methods have been proposed for semantic segmentation with outstanding results. However, these models require a lot of densely annotated images. To address this problem, we propose a new algorithm that uses Hy-perGraph Convolutional Networks for Weakly-supervised...
Article
Full-text available
Recently, Deep Neural Networks (DNNs) have become a central subject of discussion in computer vision for a broad range of applications, including image classification and face recognition. Compared to existing conventional machine learning methods, deep learning algorithms have shown prominent performance with high accuracy and speed. However, they...
Preprint
Moving Object Detection (MOD) is a fundamental step for many computer vision applications. MOD becomes very challenging when a video sequence captured from a static or moving camera suffers from the challenges: camouflage, shadow, dynamic backgrounds, and lighting variations, to name a few. Deep learning methods have been successfully applied to ad...
Preprint
Full-text available
Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing. The sampling and reconstruction of static graph signals have played a central role in GSP. However, many real-...
Article
Moving Objects Segmentation (MOS) is a crucial step in various computer vision applications, such as visual object tracking, autonomous vehicles, human activity analysis, surveillance, and security. Existing MOS approaches suffer from performance degradation due to extreme challenging conditions in real world complex environments such as varying il...
Article
Automatic ship segmentation from high-resolution Synthetic Aperture Radar (SAR) remote sensing images has been a topic of interest that has gradually gained attention over the years due to the abundance of earth observation sensors. Recently, deep learning methods have provided a breakthrough increasing the performance greatly. But, the correspondi...
Article
Full-text available
Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing. The sampling and reconstruction of static graph signals have played a central role in GSP. However, many real-...
Article
Full-text available
Recent methods for visual tracking exploit a multitude of information obtained from combinations of handcrafted and/or deep features. However, the response maps derived from these feature combinations are often fused using simple strategies such as winner-takes-all or weighted sum approaches. Although some efficient fusion methods have also been pr...
Conference Paper
Full-text available
Moving object detection has been a central topic of discussion in computer vision for its wide range of applications like in self-driving cars, video surveillance , security, and enforcement. Neuromorphic Vision Sensors (NVS) are bio-inspired sensors that mimic the working of the human eye. Unlike conventional frame-based cameras, these sensors cap...
Conference Paper
Full-text available
Moving Object Detection (MOD) is a fundamental step for many computer vision applications. MOD becomes very challenging when a video sequence captured from a static or moving camera suffers from the challenges: camouflage , shadow, dynamic backgrounds, and lighting variations , to name a few. Deep learning methods have been successfully applied to...
Preprint
Full-text available
Moving object detection has been a central topic of discussion in computer vision for its wide range of applications like in self-driving cars, video surveillance, security, and enforcement. Neuromorphic Vision Sensors (NVS) are bio-inspired sensors that mimic the working of the human eye. Unlike conventional frame-based cameras, these sensors capt...
Chapter
Background subtraction is one of the fundamental tasks for many robotics and computer vision applications. Recently, graph signal processing techniques have attained significant attention, leading to new advances and insights in the field of background subtraction for video analysis in the past years. In this chapter, we present the concept of blue...
Preprint
Full-text available
Finding the best mathematical equation to deal with the different challenges found in complex scenarios requires a thorough understanding of the scenario and a trial and error process carried out by experts. In recent years, most state-of-the-art equation discovery methods have been widely applied in modeling and identification systems. However, eq...
Conference Paper
Full-text available
Foreground segmentation is a fundamental problem in many artificial intelligence and computer vision based applications. However, robust foreground segmentation with high precision is still a challenging problem in complex scenes. Currently, many of the existing algorithms process the input data in RGB space only, where the foreground seg-mentation...
Conference Paper
Full-text available
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...
Article
Full-text available
Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to undesirable variations in the background scene, MOS becomes very challenging for static and moving camera sequences. Several deep learning methods have been proposed for MOS with impressive performance. However, these methods show performance degradation in the presen...
Article
During about 30 years, a lot of research teams have worked on the big challenge of detection of moving objects in various challenging environments. First applications concern static cameras but with the rise of the mobile sensors studies on moving cameras have emerged over time. In this survey, we propose to identify and categorize the different ex...
Conference Paper
Full-text available
Deep Convolutional Neural Networks (DCNNs) are widely used to extract high-dimensional features in various image recognition tasks and have shown significant performance in face recognition. However , accurate real-time face recognition remains a challenge, mainly due to the high computational cost associated with the use of DCNNs and the need to b...
Conference Paper
Full-text available
In this article, a novel pixel based object detection framework is proposed that leverages dual type pixel-level information to construct the background model. The first type of information is initially used intensity histograms over a training set of a few initial video frames. Finally, it is formed by gathering all the minimum and maximum values...
Conference Paper
Full-text available
Dynamic Background Subtraction (DBs) is a fundamental problem in many vision based applications. Nonetheless DBs in real complex environments has several challenging conditions like illumination variations, shadows, camera jitters and bad weathers. In this study we aim to address the problem of DBs in complex scenes by exploiting conditional least...
Conference Paper
Full-text available
Recently, several successful methods based on deep neural networks have been proposed for background subtraction. These deep neural algorithms have almost perfect performance , relying in the availability of ground-truth frames of the tested videos during the training step. However, the performance of some of these algorithms drops significantly wh...
Chapter
Mathematical tools, machine learning and signal processing tools have achieved enormous success in computer vision. In this chapter, we present a state-of-art of the progress that have occured in moving objects detection, classification and recognition in video sequences taken by fixed cameras. More specifically, we focus on the last breaktrought m...
Preprint
Full-text available
The mathematical modeling of infectious diseases is a fundamental research field for the planning of strategies to contain outbreaks. The models associated with this field of study usually have exponential prior assumptions in the number of new cases, while the exploration of spatial data has been little analyzed in these models. In this paper, we...
Article
Full-text available
In this study, the authors present a new approach to segment and classify moving objects in video sequences by combining an unsupervised anomaly discovery framework called DeepSphere and generative adversarial networks. The proposed deep detector classifier employs and validates DeepSphere, which aims mainly to identify the anomalous cases in the s...
Conference Paper
Dynamic Background Modeling (DBM) is a crucial task in many computer vision based applications such as human activity analysis, traffic monitoring, surveillance, and security. DBM is extremely challenging in scenarios like illumination changes, camouflage, intermittent object motion or shadows. In this study, we proposed an end-to-end framework bas...
Conference Paper
Full-text available
Steganography is a science which helps to hide secret data inside multimedia supports like image, audio and video files to ensure secure communication between two parts of a channel. Steganalysis is the discipline which detects the presence of data hidden by a steganographic algorithm. There are two types of steganalysis: targeted steganalysis and...
Chapter
Full-text available
Machine learning has been widely applied for detection of moving objects from static cameras. Recently, many methods using deep learning for background subtraction have been reported, with very promising performance. This chapter provides a survey of different deep-learning based background subtraction methods. First, a comparison of the architectu...
Article
Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Mo...
Preprint
Full-text available
Graph-based algorithms have been successful approaching the problems of unsupervised and semi-supervised learning. Recently, the theory of graph signal processing and semi-supervised learning have been combined leading to new developments and insights in the field of machine learning. In this paper, concepts of recovery of graph signals and semi-su...
Preprint
Full-text available
Graph-based algorithms have been successful approaching the problems of unsupervised and semi-supervised learning. Recently, the theory of graph signal processing and semi-supervised learning have been combined leading to new developments and insights in the field of machine learning. In this paper, concepts of recovery of graph signals and semi-su...
Preprint
Full-text available
During about 30 years, a lot of research teams have worked on the big challenge of detection of moving objects in various challenging environments. First applications concern static cameras but with the rise of the mobile sensors studies on moving cameras have emerged over time. In this survey, we propose to identify and categorize the different ex...
Conference Paper
Full-text available
Segmentation of moving objects from video sequences plays an important role in many computer vision applications. In this paper, we present a background subtraction approach based on deep neural networks. More speci�cally, we propose to employ and validate an unsupervised anomaly discovery framework called \DeepSphere" to perform foreground objects...
Article
Full-text available
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 met...
Preprint
Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Mo...
Article
Full-text available
The papers in this special section focus on robust subspace learning and tracking. Subspace learning theory for dimensionality reduction was initiated with the Principal Component Analysis (PCA) formulation proposed by Pearson in 1901. PCA was first widely used for data analysis in the field of psychometrics and chemometrics but today it is often t...
Preprint
Full-text available
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 met...
Article
Moving object detection is a fundamental step in various computer vision applications. Robust Principal Component Analysis (RPCA) based methods have often been employed for this task. However, the performance of these methods deteriorates in the presence of dynamic background scenes, camera jitter, camouflaged moving objects, and/or variations in i...
Article
Full-text available
The papers in this special issue introduce the reader to the theory, algorithms, and applications of principal component analysis (PCA) and its many extensions. The aim of PCA is to reduce the dimensionality of multivariate data while preserving as much of the relevant information as possible. It is often the first step in various types of explorat...
Article
Full-text available
Article
Full-text available
Principal Components Analysis (PCA) is one of the most widely used dimension reduction techniques. Given a matrix of clean data, PCA is easily accomplished via singular value decomposition (SVD) on the data matrix. While PCA for relatively clean data is an easy and solved problem, it becomes much harder if the data is corrupted by even a few outlie...
Article
Full-text available
Robust PCA (RPCA) via decomposition into low-rank plus sparse matrices offers a powerful framework for a large variety of applications such as image processing, video processing and 3D computer vision. Indeed, most of the time these applications require to detect sparse outliers from the observed imagery data that can be approximated by a low-rank...
Conference Paper
Full-text available
This paper provides a comparative theoretical and experimental evaluation of solutions for robust PCA and robust subspace tracking (dynamic RPCA) that rely on the sparse+lowrank matrix decomposition formulation. The emphasis is on simple and provably correct methods. Experimental comparisons are shown for video layering (separate a given video into...
Article
Full-text available
This paper presents a survey on the latest methods of moving object detection in video sequences captured by a moving camera. Although many researches and excellent works have reviewed the methods of object detection and background subtraction for a fixed camera, there is no survey which presents a complete review of the existing different methods...
Article
Full-text available
Background modeling has emerged as a popular foreground detection technique for various applications in video surveillance. Background modeling methods have become increasing efficient in robustly modeling the background and hence detecting moving objects in any visual scene. Although several background subtraction and foreground detection have bee...
Presentation
Full-text available
Robust Low-rank and Sparse Decomposition for Background/Foreground Separation: From Matrices to Tensors
Article
In the last decades, researchers in the field of Background Subtraction (BS) have developed methods to handle the different type of challenges. However, at the present time, no traditional algorithm seems to be able to simultaneously address all the key BS challenges. This can mainly be attributed to the lack of systematic investigation concerning...
Conference Paper
Moving Object Detection (MOD) is a fundamental step in various computer vision and video surveillance systems. Methods based on Robust Principal Component Analysis (RPCA) have often been used for MOD. If the low-rank and sparse matrices are relatively coherent, e.g., if there are similarities between the moving objects and the background regions, a...