大葉大學圖書館 |

Human re-identification[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	006.37
書名/作者:	Human re-identification/ by Ziyan Wu.
作者:	Wu, Ziyan.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xv, 104 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Computer vision.
標題:	Pattern recognition systems.
標題:	Electronic surveillance.
標題:	Computer Science.
標題:	Image Processing and Computer Vision.
標題:	Artificial Intelligence (incl. Robotics)
標題:	Multimedia Information Systems.
標題:	Computer Communication Networks.
ISBN:	9783319409917
ISBN:	9783319409900
內容註:	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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-40991-7

Human re-identification[electronic resource] /
Wu, Ziyan.

Human re-identification[electronic resource] /by Ziyan Wu. - Cham :Springer International Publishing :2016. - xv, 104 p. :ill., digital ;24 cm. - Multimedia systems and applications. - Multimedia systems and applications..

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.

ISBN: 9783319409917

Standard No.: 10.1007/978-3-319-40991-7doiSubjects--Topical Terms:

403529
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

Human re-identification[electronic resource] /
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520 $a 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.
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