Delia Velasco-Montero, Jorge Fernández-Berni, Angel Rodríguez-Vázquez

Visual Inference for IoT Systems: A Practical Approach

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: European Region Development Fund / Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-90902-4
• 1st ed. 2022
• Seitenzahl: 176
• Erscheinungstermin: 29. Januar 2022
• Englisch
• Abmessung: 241mm x 160mm x 16mm
• Gewicht: 436g
• ISBN-13: 9783030909024
• ISBN-10: 3030909026
• Artikelnr.: 62691214

Inhaltsangabe

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.

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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.