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  Maitre de Conférences (HDR)                                                                       
  Laboratoire MIA
 
Université de La Rochelle
  17000 La Rochelle
  France
  tbouwman at univ-lr.fr



Events


Workshop on "Background Learning for Detection and Tracking from RGB videos" in conjunction with ICIAP 2017, RGBD 2017,  September 2017.
(more information)

Second Workshop on  "Robust Subspace Learning and Computer Vision", ,RSL-CV 2017 in conjunction with ICCV 2017, October 2017. (More information)

Research Interests


My research interests consist mainly in the detection of moving objects in challenging environments (Background Subtraction Research). In this research, I investigated particularly the use of fuzzy concepts, discriminative subspace learning models and robust PCA in video surveillance. My work also concerns full exhaustive surveys on mathematical tools used in foreground/background separation. Furthermore, I investigated the field of decomposition in low-rank and additives matrices for background/foreground separation (DLAM Research), the field of decomposition in low-rank and additives tensors for background/foreground separation (DLAT research), and the field of decomposition in sparse and additive matrices for background/foreground separation (DSAM research). Furthermore, my research concerns also robust texture features and feature selection for background/foreground separation (Features Research). Thus, my research concerns background subtraction, fuzzy concepts, Dempster-Schafer theory, Robust Principal Component Analysis, and LBP features.


Handbook on "Background Modeling and Foreground Detection for Video Surveillance" in CRC Press



This handbook solicited contributions to address these wide range of challenges met in background modeling and foreground detection for video-surveillance. Thus, it groups the works of the leading teams in this field over the recent years. By incorporating both existing and new ideas, this handbook gives a complete overview of the concepts, theories, algorithms, and applications related to background modeling and foreground detection. First, an introduction to background modeling and foreground detection for beginners is provided by surveying statistical models, clustering models, neural networks and fuzzy models. Furthermore, leading methods and algorithms for detecting moving objects in video surveillance are presented. A description of recent complete datasets and codes are given. Moreover, an accompanying website is provided. This website contains the list of chapters, their abstract and links to the demos. It allows the reader to have a quick access to the main resources, datasets and codes in the field. Finally, with this handbook, we aim to bring a one-stop solution, i.e., access to a number of different models, algorithms, implementations and benchmarking techniques in a single volume. The handbook consists of five parts.

Publication Date :  July 1, 2014. (More information) [Purchase]


Handbook on "Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing" in  CRC Press
This handbook solicited contributions in the field of robust decomposition in low rank and sparse matrices. By incorporating both existing and new ideas, this handbook gives a complete overview of the concepts, theories, algorithms, and applications related to robust decomposition in low rank and sparse matrices. Moreover, an accompanying website is provided. This website contains the list of chapters, their abstract and links to the demos. It allows the reader to have a quick access to the main resources, datasets and codes in the field. Finally, with this handbook, we aim to bring a one-stop solution, i.e., access to a number of different decomposition, solvers, implementations and benchmarking techniques in a single volume. The handbook consists of five parts.

Publication Date :  May 30, 2016. (More information) [Purchase]


Recent Publications


T. Bouwmans, “Traditional and Recent Approaches in Background Modeling for Foreground Detection: An Overview”,
Computer Science Review,  Volume 11, pages 31-66, May 2014. [pdf]

T. Bouwmans, A. Sobral, S. Javed, S. Jung, E. Zahzah, "Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset", Computer Science Review, November 2016. [pdf]

T. Bouwmans, L. Maddalena, A. Petrosino, "
Scene Background Initialization: a Taxonomy", Pattern Recognition Letters, December 2016. [pdf]


My recent publications are available on Academia, Research Gate, Researchr, ORCID and Publication List.