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Titlebook: Video Analytics for Business Intelligence; Caifeng Shan,Fatih Porikli,Shaogang Gong Book 2012 Springer Berlin Heidelberg 2012 Business Int

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書目名稱Video Analytics for Business Intelligence
編輯Caifeng Shan,Fatih Porikli,Shaogang Gong
視頻videohttp://file.papertrans.cn/983/982897/982897.mp4
概述State of the art of Video Analytics for Business Intelligence.Demonstrates how surveillance cameras can be used for collecting statistical information for marketing e.g. in retail/shop environments.Wr
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Video Analytics for Business Intelligence;  Caifeng Shan,Fatih Porikli,Shaogang Gong Book 2012 Springer Berlin Heidelberg 2012 Business Int
描述.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 research 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 shopping 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. Video analytics has the enormous potential for non-security orien
出版日期Book 2012
關(guān)鍵詞Business Intelligence; Computational Intelligence; Computational Vision; Video Analytics
版次1
doihttps://doi.org/10.1007/978-3-642-28598-1
isbn_softcover978-3-662-52028-4
isbn_ebook978-3-642-28598-1Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer Berlin Heidelberg 2012
The information of publication is updating

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978-3-662-52028-4Springer Berlin Heidelberg 2012
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Auto-calibration of Non-overlapping Multi-camera CCTV Systemsstarting from single camera calibration thereby bringing the problem to a reduced form suitable for multi-view calibration. It extends the standard bundle adjustment by a smoothness constraint to avoid the ill-posed problem arising from missing point correspondences. The stratified optimization supp
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Fast Approximate Nearest Neighbor Methods for Example-Based Video Searchction. Then the clips with the highest similarity values can be returned as the answer-set. However, as the number of the videos in the collection grows, such computation becomes prohibitively expensive. In order to show sub-linear growth any large-scale algorithm needs to exploit some properties of
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Scene Invariant Crowd Counting and Crowd Occupancy Analysisre unseen in the training data, and be trained on significantly less data. Scene invariance is achieved through the use of camera calibration, allowing the system to be trained on one or more viewpoints and then deployed on any number of new cameras for testing without further training. A pre-traine
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