The first book, by the leading experts, on this rapidly developing field with applications to security, smart homes, multimedia, and environmental monitoring Comprehensive coverage of fundamentals, algorithms, design methodologies, system implementation issues, architectures, and applications Presents in detail the latest developments in multi-camera calibration, active and heterogeneous camera networks, multi-camera object and event detection, tracking, coding, smart camera architecture and middleware This book is the definitive reference in multi-camera networks. It gives clear guidance on the conceptual and implementation issues involved in the design and operation of multi-camera networks, as well as presenting the state-of-the-art in hardware, algorithms and system development. The book is broad in scope, covering smart camera architectures, embedded processing, sensor fusion and middleware, calibration and topology, network-based detection and tracking, and applications in distributed and collaborative methods in camera networks. This book will be an ideal reference for university researchers, R&D engineers, computer engineers, and graduate students working in signal and video processing, computer vision, and sensor networks. Hamid Aghajan is a Professor of Electrical Engineering (consulting) at Stanford University. His research is on multi-camera networks for smart environments with application to smart homes, assisted living and well being, meeting rooms, and avatar-based communication and social interactions. He is Editor-in-Chief of Journal of Ambient Intelligence and Smart Environments, and was general chair of ACM/IEEE ICDSC 2008. Andrea Cavallaro is Reader (Associate Professor) at Queen Mary, University of London (QMUL). His research is on target tracking and audiovisual content analysis for advanced surveillance and multi-sensor systems. He serves as Associate Editor of the IEEE Signal Processing Magazine and the IEEE Trans. on Multimedia, and has been general chair of IEEE AVSS 2007, ACM/IEEE ICDSC 2009 and BMVC 2009. The first book, by the leading experts, on this rapidly developing field with applications to security, smart homes, multimedia, and environmental monitoring Comprehensive coverage of fundamentals, algorithms, design methodologies, system implementation issues, architectures, and applications Presents in detail the latest developments in multi-camera calibration, active and heterogeneous camera networks, multi-camera object and event detection, tracking, coding, smart camera architecture and middleware

Camera networks have recently been proposed as a sensor modality for 3D localization and tracking tasks. Recent advances in computer vision and decreasing equipment costs have made the use of video cameras increasingly favorable. Their extensibility, unobtrusiveness, and low cost make camera networks an appealing sensor for a broad range of applications. However, due to the complex interaction between system parameters and their impact on performance, designing these systems is currently as much an art as a science. Specifically, the designer must minimize the error (where the error function may be unique to each application) by varying the camera network's configuration, all while obeying constraints imposed by scene geometry, budget, and minimum required work volume. Designers often have no objective sense of how the main parameters drive performance, resulting in a configuration based primarily on intuition. Without an objective process to search through the enormous parameter space, camera networks have enjoyed moderate success as a laboratory tool but have yet to realize their commercial potential. In this thesis we develop a systematic methodology to improve the design of multi-camera networks. First, we explore the impact of varying system parameters on performance motivated by a 3D localization task. The parameters we investigate include those pertaining to the camera (resolution, field of view, etc.), the environment (work volume and degree of occlusion) and noise sources. Ultimately, we seek to provide insights to common questions facing camera network designers: How many cameras are needed? Of what type? How should they be placed? First, to help designers efficiently explore the vast parameter spaces inherent in multi-camera network design, we develop a camera network simulation environment to rapidly evaluate potential configurations. Using this simulation, we propose a new method for camera network configuration based on genetic algorithms. Starting from an initially random population of configurations, we demonstrate how an optimal camera network configuration can be evolved, without a priori knowledge of the interdependencies between parameters. This numerical approach is adaptable to different environments or application requirements and can efficiently accommodate a high-dimensional search space, while producing superior results to hand-designed camera networks. The proposed method is both easier to implement than a hand-designed network and is more accurate, as measured by 3D point reconstruction error. Next, with the fundamentals of multi-camera network design in place, we then demonstrate how the system can be applied to a common computer vision task, namely, 3D localization and tracking. The typical approach to localization and tracking is to apply traditional 2D algorithms (that is, those designed to operate on the image plane) to multiple cameras and fuse the results. We describe a new method which takes the noise sources inherent to camera networks into account. By modeling the velocity of the tracked object in addition to position we can compensate for synchronization errors between cameras in the network, thereby reducing the localization error. Through this experiment we provide evidence that algorithms specific to multi-camera networks perform better than straightforward extensions of their single-camera counterparts. Finally, we verify the efficacy of the camera network configuration and 3D tracking algorithms by demonstrating their use in empirical experiments. The results obtained were similar to the results produced by the simulated environment.

From a basic two-camera interview to an elaborate 26 camera HD concert film, this comprehensive guide presents a platform-agnostic approach to the essential techniques required to set up and edit a multi-camera project. Actual case studies are used to examine specific usages of multi-camera editing and include a variety of genres including concerts, talk shows, reality programming, sit-coms, documentaries for television, event videography and feature films. Other features include: * Advanced multi-camera techniques and specialty work-flows are examined for tapeless & large scale productions with examples from network TV shows, corporate media projects, event videography, and feature films. * New techniques for 3D projects, 2k/4k media management and color correction are revealed. * Technical breakdowns analyze system requirements for monitoring, hard drives & RAIDs, RAM, codecs and computer platforms. * Apple Final Cut Pro, Avid Media Composer, Adobe Premiere Pro and several other software programs are detailed. * Tables, charts, screen-grabs, photos, web-links, blogs, tech school lists and other resource tools for further study. * Unique interviews with the 'Masters of Multi-Cam' including EMMY and academy award-winning directors and editors who share their project notes and give insight to award-winning techniques.

As networks of video cameras are installed in many applications like security and surveillance, environmental monitoring, disaster response, and assisted living facilities, among others, image understanding in camera networks is becoming an important area of research and technology development. There are many challenges that need to be addressed in the process. Some of them are listed below: - Traditional computer vision challenges in tracking and recognition, robustness to pose, illumination, occlusion, clutter, recognition of objects, and activities; - Aggregating local information for wide area scene understanding, like obtaining stable, long-term tracks of objects; - Positioning of the cameras and dynamic control of pan-tilt-zoom (PTZ) cameras for optimal sensing; - Distributed processing and scene analysis algorithms; - Resource constraints imposed by different applications like security and surveillance, environmental monitoring, disaster response, assisted living facilities, etc. In this book, we focus on the basic research problems in camera networks, review the current state-of-the-art and present a detailed description of some of the recently developed methodologies. The major underlying theme in all the work presented is to take a network-centric view whereby the overall decisions are made at the network level. This is sometimes achieved by accumulating all the data at a central server, while at other times by exchanging decisions made by individual cameras based on their locally sensed data. Chapter One starts with an overview of the problems in camera networks and the major research directions. Some of the currently available experimental testbeds are also discussed here. One of the fundamental tasks in the analysis of dynamic scenes is to track objects. Since camera networks cover a large area, the systems need to be able to track over such wide areas where there could be both overlapping and non-overlapping fields of view of the cameras, as addressed in Chapter Two: Distributed processing is another challenge in camera networks and recent methods have shown how to do tracking, pose estimation and calibration in a distributed environment. Consensus algorithms that enable these tasks are described in Chapter Three. Chapter Four summarizes a few approaches on object and activity recognition in both distributed and centralized camera network environments. All these methods have focused primarily on the analysis side given that images are being obtained by the cameras. Efficient utilization of such networks often calls for active sensing, whereby the acquisition and analysis phases are closely linked. We discuss this issue in detail in Chapter Five and show how collaborative and opportunistic sensing in a camera network can be achieved. Finally, Chapter Six concludes the book by highlighting the major directions for future research. Table of Contents: An Introduction to Camera Networks / Wide-Area Tracking / Distributed Processing in Camera Networks / Object and Activity Recognition / Active Sensing / Future Research Directions

Large-scale video networks are of increasing importance in a wide range of applications. However, the development of automated techniques for aggregating and interpreting information from multiple video streams in real-life scenarios is a challenging area of research. Collecting the work of leading researchers from a broad range of disciplines, this timely text/reference offers an in-depth survey of the state of the art in distributed camera networks. The book addresses a broad spectrum of critical issues in this highly interdisciplinary field: current challenges and future directions; video processing and video understanding; simulation, graphics, cognition and video networks; wireless video sensor networks, communications and control; embedded cameras and real-time video analysis; applications of distributed video networks; and educational opportunities and curriculum-development. Topics and features: presents an overview of research in areas of motion analysis, invariants, multiple cameras for detection, object tracking and recognition, and activities in video networks; provides real-world applications of distributed video networks, including force protection, wide area activities, port security, and recognition in night-time environments; describes the challenges in graphics and simulation, covering virtual vision, network security, human activities, cognitive architecture, and displays; examines issues of multimedia networks, registration, control of cameras (in simulations and real networks), localization and bounds on tracking; discusses system aspects of video networks, with chapters on providing testbed environments, data collection on activities, new integrated sensors for airborne sensors, face recognition, and building sentient spaces; investigates educational opportunities and curriculum development from the perspective of computer science and electrical engineering. This unique text will be of great interest to researchers and graduate students of computer vision and pattern recognition, computer graphics and simulation, image processing and embedded systems, and communications, networks and controls. The large number of example applications will also appeal to application engineers.

This book constitutes the refereed proceedings of the 25th Symposium of the German Association for Pattern Recognition, DAGM 2003, held in Magdeburg, Germany in September 2003. The 74 revised papers presented were carefully reviewed and selected from more than 140 submissions. The papers address all current issues in pattern recognition and are organized in sections on image analyses, callibration and 3D shape, recognition, motion, biomedical applications, and applications.

This book constitutes the proceedings of the 20th INternational Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2020, held in Auckland, New Zealand, in February 2020. The 48 papers presented in this volume were carefully reviewed and selected from a total of 78 submissions. They were organized in topical sections named: deep learning; biomedical image analysis; biometrics and identification; image analysis; image restauration, compression and watermarking; tracking, and mapping and scene analysis.