Visual Inference for IoT Systems: A Practical Approach

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.

Surveys the state-of-the-art of embedded vision based on deep learning Describes strategies to leverage the limited resources of IoT devices Offers detailed examples of deep-learning-based realizations of visual inference

Contenu
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. <p