Paolo Remagnino – författare

There is a growing interest in the development and deployment of surveillance systems in public and private locations. Conventional approaches rely on the installation of wide area CCTV (Closed Circuit Television), but the explosion in the numbers of cameras that have to be monitored, the increasing costs of providing monitoring personnel and the limitations that humans have to maintain sustained levels of concentration severely limit the effectiveness of these systems. Advances in information and communication technologies, such as computer vision for face recognition and human behaviour analysis, digital annotation and storage of video, transmission of video/audio streams over wired and wireless networks, can potentially provide significant improvements in this field.The book consists of a coherent selection of extended versions of presentations made in two successful IEE symposia on Intelligent Distributed Surveillance Systems (IDSS). It surveys recent development in distributed intelligent surveillance systems and brings together the work of researchers and engineers, system integrators and managers of public and private organisations likely to use such systems.

Relatively new research ?elds such as ambient intelligence, intelligent envir- ments, ubiquitous computing, and wearable devices have emerged in recent years. These ?elds are related by a common theme: making use of novel technologies to enhance user experience by providing user-centric intelligent environments, - moving computers from the desktop and making computing available anywhere and anytime. It must be said that the concept of intelligent environments is not new and beganwithhomeautomation. Thechoiceofnameforthe?eldvariessomewhatfrom continent to continent in the English-speaking world. In general intelligent space is synonymous to intelligent environments or smart spaces of which smart homes is a sub?eld. In this collection, the terms intelligent environments and ambient int- ligence are used interchangeably throughout. Such environments are made possible by permeating living spaces with intelligent technology that enhances quality of life. In particular, advances in technologies such as miniaturized sensors, advances in communication and networking technology including high-bandwidth wireless devices and the reduction in power consumption have made possible the concept of intelligent environments. Environments such as a home, an of?ce, a shopping mall, and a travel port utilize data provided by users to adapt the environment to meet the user’s needs and improve human-machine interactions. The user information is gathered either via wearable devices or by pervasive sensors or a combination of both. Intelligent environments brings together a number of research ?elds from computer science, such as arti?cial intelligence, computer vision, machine learning, and robotics as well as engineering and architecture.

Relatively new research ?elds such as ambient intelligence, intelligent envir- ments, ubiquitous computing, and wearable devices have emerged in recent years. These ?elds are related by a common theme: making use of novel technologies to enhance user experience by providing user-centric intelligent environments, - moving computers from the desktop and making computing available anywhere and anytime. It must be said that the concept of intelligent environments is not new and beganwithhomeautomation. Thechoiceofnameforthe?eldvariessomewhatfrom continent to continent in the English-speaking world. In general intelligent space is synonymous to intelligent environments or smart spaces of which smart homes is a sub?eld. In this collection, the terms intelligent environments and ambient int- ligence are used interchangeably throughout. Such environments are made possible by permeating living spaces with intelligent technology that enhances quality of life. In particular, advances in technologies such as miniaturized sensors, advances in communication and networking technology including high-bandwidth wireless devices and the reduction in power consumption have made possible the concept of intelligent environments. Environments such as a home, an of?ce, a shopping mall, and a travel port utilize data provided by users to adapt the environment to meet the user’s needs and improve human-machine interactions. The user information is gathered either via wearable devices or by pervasive sensors or a combination of both. Intelligent environments brings together a number of research ?elds from computer science, such as arti?cial intelligence, computer vision, machine learning, and robotics as well as engineering and architecture.

Relatively new research ?elds such as ambient intelligence, intelligent envir- ments, ubiquitous computing, and wearable devices have emerged in recent years. These ?elds are related by a common theme: making use of novel technologies to enhance user experience by providing user-centric intelligent environments, - moving computers from the desktop and making computing available anywhere and anytime. It must be said that the concept of intelligent environments is not new and beganwithhomeautomation. Thechoiceofnameforthe?eldvariessomewhatfrom continent to continent in the English-speaking world. In general intelligent space is synonymous to intelligent environments or smart spaces of which smart homes is a sub?eld. In this collection, the terms intelligent environments and ambient int- ligence are used interchangeably throughout. Such environments are made possible by permeating living spaces with intelligent technology that enhances quality of life. In particular, advances in technologies such as miniaturized sensors, advances in communication and networking technology including high-bandwidth wireless devices and the reduction in power consumption have made possible the concept of intelligent environments. Environments such as a home, an of?ce, a shopping mall, and a travel port utilize data provided by users to adapt the environment to meet the user’s needs and improve human-machine interactions. The user information is gathered either via wearable devices or by pervasive sensors or a combination of both. Intelligent environments brings together a number of research ?elds from computer science, such as arti?cial intelligence, computer vision, machine learning, and robotics as well as engineering and architecture.

Video context analysis is an active and vibrant research area, which provides means for extracting, analyzing and understanding behavior of a single target and multiple targets. Over the last few decades, computer vision researchers have been working to improve the accuracy and robustness of algorithms to analyse the context of a video automatically. In general, the research work in this area can be categorized into three major topics: 1) counting number of people in the scene 2) tracking individuals in a crowd and 3) understanding behavior of a single target or multiple targets in the scene. This book focusses on tracking individual targets and detecting abnormal behavior of a crowd in a complex scene. Firstly, this book surveys the state-of-the-art methods for tracking multiple targets in a complex scene and describes the authors' approach for tracking multiple targets. The proposed approach is to formulate the problem of multi-target tracking as an optimization problem of finding dynamic optima (pedestrians) where these optima interact frequently. A novel particle swarm optimization (PSO) algorithm that uses a set of multiple swarms is presented. Through particles and swarms diversification, motion prediction is introduced into the standard PSO, constraining swarm members to the most likely region in the search space. The social interaction among swarm and the output from pedestrians-detector are also incorporated into the velocity-updating equation. This allows the proposed approach to track multiple targets in a crowded scene with severe occlusion and heavy interactions among targets. The second part of this book discusses the problem of detecting and localising abnormal activities in crowded scenes. We present a spatio-temporal Laplacian Eigenmap method for extracting different crowd activities from videos. This method learns the spatial and temporal variations of local motions in an embedded space and employs representatives of different activities to construct the model which characterises the regular behavior of a crowd. This model of regular crowd behavior allows for the detection of abnormal crowd activities both in local and global context and the localization of regions which show abnormal behavior.

Video context analysis is an active and vibrant research area, which provides means for extracting, analyzing and understanding behavior of a single target and multiple targets. Over the last few decades, computer vision researchers have been working to improve the accuracy and robustness of algorithms to analyse the context of a video automatically. In general, the research work in this area can be categorized into three major topics: 1) counting number of people in the scene 2) tracking individuals in a crowd and 3) understanding behavior of a single target or multiple targets in the scene. This book focusses on tracking individual targets and detecting abnormal behavior of a crowd in a complex scene. Firstly, this book surveys the state-of-the-art methods for tracking multiple targets in a complex scene and describes the authors'' approach for tracking multiple targets. The proposed approach is to formulate the problem of multi-target tracking as an optimization problem of finding dynamic optima (pedestrians) where these optima interact frequently. A novel particle swarm optimization (PSO) algorithm that uses a set of multiple swarms is presented. Through particles and swarms diversification, motion prediction is introduced into the standard PSO, constraining swarm members to the most likely region in the search space. The social interaction among swarm and the output from pedestrians-detector are also incorporated into the velocity-updating equation. This allows the proposed approach to track multiple targets in a crowded scene with severe occlusion and heavy interactions among targets. The second part of this book discusses the problem of detecting and localising abnormal activities in crowded scenes. We present a spatio-temporal Laplacian Eigenmap method for extracting different crowd activities from videos. This method learns the spatial and temporal variations of local motions in an embedded space and employs representatives of different activities to construct the model which characterises the regular behavior of a crowd. This model of regular crowd behavior allows for the detection of abnormal crowd activities both in local and global context and the localization of regions which show abnormal behavior.

This book is a continuation of our previous volumes on Innovations in Defence Support Systems. This book includes a sample of recent advances in intelligent monitoring. The contributions include:  ·         Data fusion in modern surveillance·         Distributed intelligent surveillance systems modeling for performance evaluation·         Incremental learning on trajectory clustering·         Pedestrian speed profiles from video sequence·         System-wide tracking of individuals·         A scalable approach based on normality components for intelligent surveillance·         Distributed camera overlap estimation·         Multi-robot team for environmental monitoring The book is directed to the security experts, engineers, scientists, students and professors who are interested in intelligent monitoring.

This book is a continuation of our previous volumes on Innovations in Defence Support Systems. This book includes a sample of recent advances in intelligent monitoring. The contributions include:  ·         Data fusion in modern surveillance·         Distributed intelligent surveillance systems modeling for performance evaluation·         Incremental learning on trajectory clustering·         Pedestrian speed profiles from video sequence·         System-wide tracking of individuals·         A scalable approach based on normality components for intelligent surveillance·         Distributed camera overlap estimation·         Multi-robot team for environmental monitoring The book is directed to the security experts, engineers, scientists, students and professors who are interested in intelligent monitoring.

This book discusses innovative methods for mining information from images of plants, especially leaves, and highlights the diagnostic features that can be implemented in fully automatic systems for identifying plant species. Adopting a multidisciplinary approach, it explores the problem of plant species identification, covering both the concepts of taxonomy and morphology. It then provides an overview of morphometrics, including the historical background and the main steps in the morphometric analysis of leaves together with a number of applications. The core of the book focuses on novel diagnostic methods for plant species identification developed from a computer scientist’s perspective. It then concludes with a chapter on the characterization of botanists' visions, which highlights important cognitive aspects that can be implemented in a computer system to more accurately replicate the human expert’s fixation process. The book not only represents an authoritative guide to advanced computational tools for plant identification, but provides experts in botany, computer science and pattern recognition with new ideas and challenges. As such it is expected to foster both closer collaborations and further technological developments in the emerging field of automatic plant identification.

This book discusses innovative methods for mining information from images of plants, especially leaves, and highlights the diagnostic features that can be implemented in fully automatic systems for identifying plant species. Adopting a multidisciplinary approach, it explores the problem of plant species identification, covering both the concepts of taxonomy and morphology. It then provides an overview of morphometrics, including the historical background and the main steps in the morphometric analysis of leaves together with a number of applications. The core of the book focuses on novel diagnostic methods for plant species identification developed from a computer scientist’s perspective. It then concludes with a chapter on the characterization of botanists'' visions, which highlights important cognitive aspects that can be implemented in a computer system to more accurately replicate the human expert’s fixation process. The book not only represents an authoritative guide to advanced computational tools for plant identification, but provides experts in botany, computer science and pattern recognition with new ideas and challenges. As such it is expected to foster both closer collaborations and further technological developments in the emerging field of automatic plant identification.