Название: Visual Inference for IoT Systems: A Practical Approach
Автор: Delia Velasco-Montero, Jorge Fernandez-Berni
Издательство: Springer
Год: 2022
Страниц: 171
Язык: английский
Формат: pdf (true), epub
Размер: 34.1 MB

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.

In this monograph, we have put all our experience and knowledge into play in order to provide a systematic and up-to-date approach to vision-enabled embedded systems, and a comprehensive analysis of their implementation specificities. Practical aspects are profusely detailed. Guidelines are presented for optimal selection of hardware and software components according to prescribed application requirements. The book includes a remarkable set of experimental results and functional procedures supporting the theoretical concepts and methodologies introduced. A case study is also presented.

All the contents revolve around the state-of-the-art computer vision paradigm, i.e., DL. These days, accurate, but computationally heavy, neural networks are employed for a variety of tasks. The strong processing and memory requirements of such networks come into conflict with the limited availability of resources in IoT devices. Meanwhile, a great deal of technological components has been released to support DL deployments. Each of these components claims advantages in terms of performance, system integration, energy consumption, etc. The book provides tools to navigate this complex scenario in an effective manner.

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.

Content:
1. Introduction
2. Embedded Vision for the Internet of Things: A Survey on State-of-the-Art Hardware, Software, and Deep Learning Models
3. Optimal Selection of Software and Models for Visual Inference
4. Relevant Hardware Metrics for Performance Evaluation
5. Prediction of Visual Inference Performance
6. A Case Study: Remote Animal Monitoring