Lin L., Zhang D., Luo P., Zuo W. Human Centric Visual Analysis with Deep Learning

Springer, 2020. — 160 p. — ISBN: 978-981-13-2386-7.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.The Foundation and Advances of Deep Learning
Human-Centric Visual Analysis: Tasks and Progress
Face Localization and Enhancement
Pedestrian Detection with RPN and Boosted Forest
Self-supervised Structure-Sensitive Learning for Human Parsing
Instance-Level Human Parsing
Video Instance-Level Human Parsing
Person Verification
Face Verification
Human Activity Understanding