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Visual inference for IoT systems [electronic resource] : a practical approach / Delia Velasco-Montero, Jorge Fernández-Berni, Angel Rodríguez-Vázquez.

Velasco-Montero, Delia, author.; Fernández-Berni, Jorge, author.; Rodríguez Vázquez, Ángel, author.
2022
TA1634
Available Online
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Title
Visual inference for IoT systems [electronic resource] : a practical approach / Delia Velasco-Montero, Jorge Fernández-Berni, Angel Rodríguez-Vázquez.
Author
Velasco-Montero, Delia, author.
ISBN
9783030909031 (electronic bk.)
3030909034 (electronic bk.)
3030909026
9783030909024
DOI
https://doi.org/10.1007/978-3-030-90903-1
Published
Cham, Switzerland : Springer, 2022.
Language
English
Description
1 online resource
Item Number
10.1007/978-3-030-90903-1 doi
Call Number
TA1634
Dewey Decimal Classification
006.3/7
Summary
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.
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Includes bibliographical references.
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Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 9, 2022).
Added Author
Fernández-Berni, Jorge, author.
Rodríguez Vázquez, Ángel, author.
Available in Other Form
Print version: 9783030909024
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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.

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