Springer Person Re-Identification (Advances in Computer Vision and Pattern Recognition)

The first book of its kind dedicated to the challenge of person re-identification, this text provides an in-depth, multidisciplinary discussion of recent developments and state-of-the-art methods. Features: introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms and examines the benefits of semantic attributes; describes how to segregate meaningful body parts from background clutter; examines the use of 3D depth images and contextual constraints derived from the visual appearance of a group; reviews approaches to feature transfer function and distance metric learning and discusses potential solutions to issues of data scalability and identity inference; investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference and describes techniques for improving post-rank search efficiency; explores the design rationale and implementation considerations of building a practical re-identification system. Review The book is intended for researchers that work, or want to start working on this research topic. … The book is very interesting and allows having an insight into the problem of person re-identification and its potential applications. It provides a presentation of the current state-of-the-are and recent progress on this topic. I think that researchers who intend to work on re-identification can benefit from reading this book. They will be introduced to the many interesting challenges to be faced.” (Donatello Conte, IAPR Newsletter, Vol. 37 (2), 2015) From the Back Cover Re-identification offers a useful tool for non-invasive biometric validation, surveillance, and human-robot interaction in a broad range of applications from crowd traffic management to personalised healthcare. This comprehensive volume is the first work of its kind dedicated to addressing the challenge of Person Re-Identification, presenting insights from an international selection of leading authorities in the field. Taking a strongly multidisciplinary approach, the text provides an in-depth discussion of recent developments and state-of-the-art methods drawn from the computer vision, pattern recognition and machine learning communities, embracing both fundamental research and practical applications. Topics and features: Introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms, and examines the benefits of semantic attributes Describes how to segregate meaningful body parts from background clutter Examines the use of 3D depth images, and contextual constraints derived from the visual appearance of a group Reviews approaches to feature transfer function and distance metric learning, and discusses potential solutions to issues of data scalability and identity inference Investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference, and describes techniques for improving post-rank search efficiency Explores the design rationale and implementation considerations of building a practical re-identification system This timely collection will be of great interest to academics, industrial researchers and postgraduates involved in computer vision and machine learning, database image retrieval, big data mining, and search engines, as well as to developers keen to exploit this emerging technology for commercial applications. About the Author Dr. Shaogang Gong is a Professor of Visual Computation in the School of Electronic Engineering and Computer Science at Queen Mary University of London, UK. His publications include the successful Springer books Visual Analysis of Behaviour and Video Analytics for Business Intelligence. Dr. Marco Cristani is an Assistant Professor in the Computer Science Department at the University of Verona, Italy. Dr. Shuicheng Yan is an Associate Professor in the Department of Electrical and Computer Enginee