Design And Performance Of Multi Camera Networks
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Design and Performance of Multi-camera Networks
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.
Multi-Camera Networks
- 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
Computer Vision – ECCV 2024
The multi-volume set of LNCS books with volume numbers 15059 upto 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024. The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as Computer vision, Machine learning, Deep neural networks, Reinforcement learning, Object recognition, Image classification, Image processing, Object detection, Semantic segmentation, Human pose estimation, 3D reconstruction, Stereo vision, Computational photography, Neural networks, Image coding, Image reconstruction and Motion estimation.