The Problem of Human re-identification -- Features and Signatures -- Multi-Object Tracking -- Surveillance Camera and its Calibration -- Calibrating a Surveillance Camera Network -- Learning Viewpoint Invariant Signatures -- Learning Subject-Discriminative Features -- Dimension Reduction with Random Projections -- Sample Selection for Multi-shot Human Reidentification -- Conclusions and Future Work
Summary
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
