Human Re-Identification [recurso electrónico] / by Ziyan Wu.
Tipo de material: TextoSeries Multimedia Systems and ApplicationsEditor: Cham : Springer International Publishing : Imprint: Springer, 2016Descripción: XV, 104 p. 40 illus. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783319409917Tema(s): Computer science | Computer communication systems | Multimedia information systems | Artificial intelligence | Image processing | Computer Science | Image Processing and Computer Vision | Artificial Intelligence (incl. Robotics) | Multimedia Information Systems | Computer Communication NetworksFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 006.6 | 006.37 Clasificación LoC:TA1637-1638TA1634Recursos en línea: Libro electrónicoTipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
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Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | 1 | No para préstamo |
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.
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.