Visual inference for IOT systems : a practical approach
Velasco-Montero, Delia;
Visual inference for IOT systems : a practical approach Delia Velasco-Montero; Jorge Fernández-Berni; Angel Rodríguez-Vázquez - Cham, Switzerland : Springer, 2022. - 159 pages : ill.; 24 cm.
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.
9783030909024
Artificial intelligence
Computer vision
Internet of things
006.37 / VEL
Visual inference for IOT systems : a practical approach Delia Velasco-Montero; Jorge Fernández-Berni; Angel Rodríguez-Vázquez - Cham, Switzerland : Springer, 2022. - 159 pages : ill.; 24 cm.
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.
9783030909024
Artificial intelligence
Computer vision
Internet of things
006.37 / VEL