Liang Lin – författare

This book introduces the applications of deep learning in various human centric visual analysis tasks, including classical ones like face detection and alignment and some newly rising tasks like fashion clothing parsing. Starting from an overview of current research in human centric visual analysis, the book then presents a tutorial of basic concepts and techniques of deep learning. In addition, the book systematically investigates the main human centric analysis tasks of different levels, ranging from detection and segmentation to parsing and higher-level understanding. At last, it presents the state-of-the-art solutions based on deep learning for every task, as well as providing sufficient references and extensive discussions. Specifically, this book addresses four important research topics, including 1) localizing persons in images, such as face and pedestrian detection; 2) parsing persons in details, such as human pose and clothing parsing, 3) identifying and verifying persons, such as face and human identification, and 4) high-level human centric tasks, such as person attributes and human activity understanding.This book can serve as reading material and reference text for academic professors / students or industrial engineers working in the field of vision surveillance, biometrics, and human-computer interaction, where human centric visual analysis are indispensable in analysing human identity, pose, attributes, and behaviours for further understanding.

This book introduces the applications of deep learning in various human centric visual analysis tasks, including classical ones like face detection and alignment and some newly rising tasks like fashion clothing parsing. Starting from an overview of current research in human centric visual analysis, the book then presents a tutorial of basic concepts and techniques of deep learning. In addition, the book systematically investigates the main human centric analysis tasks of different levels, ranging from detection and segmentation to parsing and higher-level understanding. At last, it presents the state-of-the-art solutions based on deep learning for every task, as well as providing sufficient references and extensive discussions.

Specifically, this book addresses four important research topics, including 1) localizing persons in images, such as face and pedestrian detection; 2) parsing persons in details, such as human pose and clothing parsing, 3) identifying and verifying persons, such as face and human identification, and 4) high-level human centric tasks, such as person attributes and human activity understanding.

This book can serve as reading material and reference text for academic professors / students or industrial engineers working in the field of vision surveillance, biometrics, and human-computer interaction, where human centric visual analysis are indispensable in analysing human identity, pose, attributes, and behaviours for further understanding.

This book provides a systematic overview of the mainstream technical approaches in embodied intelligence, constructing a comprehensive knowledge framework. It offers in-depth explanations of key embodied AI technologies, including perception, navigation, manipulation, and planning, as well as multi-agent interaction, communication, and cooperation. By doing so, it establishes a structured learning architecture for researchers, engineers, and students in the fields of robotics and embodied AI.This book is divided into eight chapters, with the content organized in a progressive manner from basic to advanced concepts, aiming to build a comprehensive cognitive framework for readers in the field of embodied intelligence, covering both theoretical systems and practical methods. Chapter 2 focuses on the foundational technical system of embodied intelligence, highlighting core content such as 3D spatial representation, reinforcement learning methods, and large model technologies. These foundational technologies form the theoretical backbone of current embodied intelligence algorithms, and a deep understanding of them is a crucial prerequisite for grasping the subsequent content. Chapters 3 to 7 systematically introduce the key technical systems of embodied intelligence from five dimensions: perception, navigation, manipulation, planning, and collaboration. Chapter 8, oriented towards practice, focuses on the implementation paths of embodied intelligence in simulation platforms.The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.