This book covers aspects of human re-identification problems related to computer vision and machine learning. Working from
a practical perspective, it introduces novel algorithms and designs for human re-identification that bridge the gap between
research and reality. The primary focus is on building a robust, reliable, distributed and scalable smart surveillance system
that can be deployed in real-world scenarios. This book also includes detailed discussions on pedestrian candidates detection,
discriminative feature extraction and selection, dimension reduction, distance/metric learning, and decision/ranking enhancement.This
book is intended for professionals and researchers working in computer vision and machine learning. Advanced-level students
of computer science will also find the content valuable.