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Video Analytics for Business Intelligence(视频分析及商业化应用)
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内容 第一部分:计算机视觉 对象检测和跟踪 基于实例的快速近似最近邻法 视频搜索 第二部分:人口统计 人类年龄估计与性别分类 室内大多数静态人物的计数 场景不变人群数量与人群占有率分析 用软件识别顾客行为和停留时间 生物识别技术 从检测到的功能模块生成自动活动轮廓 视频场景分析的区域、 、、、、、、、、、、、、、、、、、、、、、、、、、、、
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Studies in Computational Intelligence 409
Editor-in-Chief
Prof. Janusz Kacprzyk
Systems Research Institute
Polish Academy of Sciences
ul. Newelska 6
01-447 Warsaw
Poland
E-mail: kacprzyk@ibspan.waw.pl
For further volumes:
http://www.springer.com/series/7092
Caifeng Shan, Fatih Porikli, Tao Xiang,
and Shaogang Gong (Eds.)
Video Analytics for
Business Intelligence
ABC
Editors
Dr. Caifeng Shan
Philips Research
Eindhoven
The Netherlands
Dr. Fatih Porikli
Mitsubishi Electric Research Laboratories
Cambridge
USA
Dr. Tao Xiang
Dept. Computer Science
Queen Mary University of London
UK
Prof. Shaogang Gong
Dept. Computer Science
Queen Mary University of London
UK
ISSN 1860-949X e-ISSN 1860-9503
ISBN 978-3-642-28597-4 e-ISBN 978-3-642-28598-1
DOI 10.1007/978-3-642-28598-1
Springer Heidelberg New York Dordrecht London
Library of Congress Control Number: 2012933768
c
Springer-Verlag Berlin Heidelberg 2012
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of
the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,
broadcasting, reproduction on microfilms or in any other physical way, and transmission or information
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now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection
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and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of
this publication or parts thereof is permitted only under the provisions of the Copyright Law of the
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Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations
are liable to prosecution under the respective Copyright Law.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication
does not imply, even in the absence of a specific statement, that such names are exempt from the relevant
protective laws and regulations and therefore free for general use.
While the advice and information in this book are believed to be true and accurate at the date of pub-
lication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any
errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect
to the material contained herein.
Printed on acid-free paper
Springer is part of Springer Science+Business Media (www.springer.com)
Preface
Closed Circuit TeleVision (CCTV) cameras have been increasingly deployed
pervasively in public spaces including retail centres and shopping malls. Intelligent
video analytics aims to automatically analyze content of massive amount of public
space video data and has been one of the most active areas of computer vision re-
search in the last two decades. Current focus of video analytics research has been
largely on detecting alarm events and abnormal behaviours for public safety and
security applications. However, increasingly CCTV installations have also been
exploited for gathering and analyzing business intelligence information, in order to
enhance marketing and operational efficiency. For example, in retail environments,
surveillance cameras can be utilised to collect statistical information about shop-
ping behaviour and preference for marketing (e.g., how many people entered a
shop; how many females/males or which age groups of people showed interests to
a particular product; how long did they stay in the shop; and what are the frequent
paths), and to measure operational efficiency for improving customer experience.
In an airport, there is an urgent need for real-time measuring of congestion and
queuing length in the departure security checking area and immigration control
area, for improving efficiency and customer satisfaction. Despite the enormous po-
tential for non-security oriented commercial applications, video analytics for busi-
ness intelligence gathering and analysis has just started to receive attention and
remains an under-explored area in the research community.
Compared to security oriented visual surveillance, business intelligence driven
video analysis applications have different requirements and characteristics. For
example, in security applications it is crucial to detect abnormal or suspicious be-
haviors, often in real-time. On the contrary, business intelligence applications fo-
cus on monitoring normal events (e.g., people entering a shop), and it is often
sufficient to capture and store the observation for offline analysis. Furthermore,
for security applications, emphasis is put on accurately evaluating each individual
event. In contrast, business intelligence information is normally collected based
on events measured statistically and holistically over a long period. Due to these
differences, existing algorithms developed for security related applications may
not be readily appliable to business intelligence applications. Innovative adapta-
tion of existing techniques and/or development of novel approaches are required.
This book presents the latest developments on video analytics for business in-
telligence applications. It provides both academic and commercial practitioners an
understanding of the state-of-the-art and a resource for potential applications and
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