Visual Inference for IoT Systems : A Practical Approach
Velasco-Montero, Delia
Visual Inference for IoT Systems : A Practical Approach by Delia Velasco-Montero, Jorge Fernández-Berni, Angel Rodríguez-Vázquez - First edition 2022 - 1 recurso en línea (XIII, 159 páginas) 59 ilustraciones, 57 ilustraciones a color
Introduction -- Embedded Vision for the Internet of the Things: State-of-the-Art -- Hardware, Software, and Network Models for Deep-Learning Vision: A Survey -- Optimal Selection of Software and Models for Visual Interference -- Relevant Hardware Metrics for Performance Evaluation -- Prediction of Visual Interference Performance -- A Case Study: Remote Animal Recognition.
This book presents a systematic approach to the implementation of Internet of Things (IoT) devices achieving visual inference through deep neural networks. Practical aspects are covered, with a focus on providing guidelines to optimally select hardware and software components as well as network architectures according to prescribed application requirements. The monograph includes a remarkable set of experimental results and functional procedures supporting the theoretical concepts and methodologies introduced. A case study on animal recognition based on smart camera traps is also presented and thoroughly analyzed. In this case study, different system alternatives are explored and a particular realization is completely developed. Illustrations, numerous plots from simulations and experiments, and supporting information in the form of charts and tables make Visual Inference and IoT Systems: A Practical Approach a clear and detailed guide to the topic. It will be of interest to researchers, industrial practitioners, and graduate students in the fields of computer vision and IoT.
9783030909031
10.1007/978-3-030-90903-1 doi
Internet de los objetos
Gráficos de ordenador
TK5105.8857 / 2022
Visual Inference for IoT Systems : A Practical Approach by Delia Velasco-Montero, Jorge Fernández-Berni, Angel Rodríguez-Vázquez - First edition 2022 - 1 recurso en línea (XIII, 159 páginas) 59 ilustraciones, 57 ilustraciones a color
Introduction -- Embedded Vision for the Internet of the Things: State-of-the-Art -- Hardware, Software, and Network Models for Deep-Learning Vision: A Survey -- Optimal Selection of Software and Models for Visual Interference -- Relevant Hardware Metrics for Performance Evaluation -- Prediction of Visual Interference Performance -- A Case Study: Remote Animal Recognition.
This book presents a systematic approach to the implementation of Internet of Things (IoT) devices achieving visual inference through deep neural networks. Practical aspects are covered, with a focus on providing guidelines to optimally select hardware and software components as well as network architectures according to prescribed application requirements. The monograph includes a remarkable set of experimental results and functional procedures supporting the theoretical concepts and methodologies introduced. A case study on animal recognition based on smart camera traps is also presented and thoroughly analyzed. In this case study, different system alternatives are explored and a particular realization is completely developed. Illustrations, numerous plots from simulations and experiments, and supporting information in the form of charts and tables make Visual Inference and IoT Systems: A Practical Approach a clear and detailed guide to the topic. It will be of interest to researchers, industrial practitioners, and graduate students in the fields of computer vision and IoT.
9783030909031
10.1007/978-3-030-90903-1 doi
Internet de los objetos
Gráficos de ordenador
TK5105.8857 / 2022