Antoine Vacavant – författare

Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments. This requires effective methods for dealing with dynamic backgrounds and illumination changes as well as algorithms that must meet real-time and low memory requirements.Incorporating both established and new ideas, Background Modeling and Foreground Detection for Video Surveillance provides a complete overview of the concepts, algorithms, and applications related to background modeling and foreground detection. Leaders in the field address a wide range of challenges, including camera jitter and background subtraction.The book presents the top methods and algorithms for detecting moving objects in video surveillance. It covers statistical models, clustering models, neural networks, and fuzzy models. It also addresses sensors, hardware, and implementation issues and discusses the resources and datasets required for evaluating and comparing background subtraction algorithms. The datasets and codes used in the text, along with links to software demonstrations, are available on the book’s website.A one-stop resource on up-to-date models, algorithms, implementations, and benchmarking techniques, this book helps researchers and industry developers understand how to apply background models and foreground detection methods to video surveillance and related areas, such as optical motion capture, multimedia applications, teleconferencing, video editing, and human–computer interfaces. It can also be used in graduate courses on computer vision, image processing, real-time architecture, machine learning, or data mining.

Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments. This requires effective methods for dealing with dynamic backgrounds and illumination changes as well as algorithms that must meet real-time and low memory requirements.Incorporating both establish

Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments. This requires effective methods for dealing with dynamic backgrounds and illumination changes as well as algorithms that must meet real-time and low memory requirements.Incorporating both established and new ideas, Background Modeling and Foreground Detection for Video Surveillance provides a complete overview of the concepts, algorithms, and applications related to background modeling and foreground detection. Leaders in the field address a wide range of challenges, including camera jitter and background subtraction.The book presents the top methods and algorithms for detecting moving objects in video surveillance. It covers statistical models, clustering models, neural networks, and fuzzy models. It also addresses sensors, hardware, and implementation issues and discusses the resources and datasets required for evaluating and comparing background subtraction algorithms. The datasets and codes used in the text, along with links to software demonstrations, are available on the book’s website.A one-stop resource on up-to-date models, algorithms, implementations, and benchmarking techniques, this book helps researchers and industry developers understand how to apply background models and foreground detection methods to video surveillance and related areas, such as optical motion capture, multimedia applications, teleconferencing, video editing, and human–computer interfaces. It can also be used in graduate courses on computer vision, image processing, real-time architecture, machine learning, or data mining.

Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments. This requires effective methods for dealing with dynamic backgrounds and illumination changes as well as algorithms that must meet real-time and low memory requirements.Incorporating both establish

This book constitutes the refereed proceedings of the 6th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023, held in Derby, UK, during December 2023, in collaboration with the Applied AI Research Lab at the University of South Dakota.The 62 full papers included in this book were carefully reviewed and selected from 216 submissions. The papers are organized in the following topical sections:Volume I:Artificial intelligence and applied machine learning; applied image processing and pattern recognition; and biometrics and applications.Volume II:Healthcare informatics; pattern recognition in blockchain, IOT, cyber plus network security, and cryptography.

This book constitutes the refereed proceedings of the 6th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023, held in Derby, UK, during December 2023, in collaboration with the Applied AI Research Lab at the University of South Dakota.The 62 full papers included in this book were carefully reviewed and selected from 216 submissions. The papers are organized in the following topical sections:Volume I:Artificial intelligence and applied machine learning; applied image processing and pattern recognition; and biometrics and applications.Volume II:Healthcare informatics; pattern recognition in blockchain, IOT, cyber plus network security, and cryptography.

This book constitutes the refereed proceedings of the 6th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023, held in Derby, UK, during December 2023, in collaboration with the Applied AI Research Lab at the University of South Dakota.The 62 full papers included in this book were carefully reviewed and selected from 216 submissions. The papers are organized in the following topical sections:Volume I:Artificial intelligence and applied machine learning; applied image processing and pattern recognition; and biometrics and applications.Volume II:Healthcare informatics; pattern recognition in blockchain, IOT, cyber plus network security, and cryptography.