找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

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

[復制鏈接]
查看: 16329|回復: 49
樓主
發(fā)表于 2025-3-21 16:12:17 | 只看該作者 |倒序瀏覽 |閱讀模式
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

書目名稱Video Analytics for Business Intelligence影響因子(影響力)




書目名稱Video Analytics for Business Intelligence影響因子(影響力)學科排名




書目名稱Video Analytics for Business Intelligence網絡公開度




書目名稱Video Analytics for Business Intelligence網絡公開度學科排名




書目名稱Video Analytics for Business Intelligence被引頻次




書目名稱Video Analytics for Business Intelligence被引頻次學科排名




書目名稱Video Analytics for Business Intelligence年度引用




書目名稱Video Analytics for Business Intelligence年度引用學科排名




書目名稱Video Analytics for Business Intelligence讀者反饋




書目名稱Video Analytics for Business Intelligence讀者反饋學科排名




單選投票, 共有 0 人參與投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用戶組沒有投票權限
沙發(fā)
發(fā)表于 2025-3-21 21:44:47 | 只看該作者
板凳
發(fā)表于 2025-3-22 03:42:55 | 只看該作者
978-3-662-52028-4Springer Berlin Heidelberg 2012
地板
發(fā)表于 2025-3-22 07:20:13 | 只看該作者
5#
發(fā)表于 2025-3-22 12:21:50 | 只看該作者
6#
發(fā)表于 2025-3-22 14:20:00 | 只看該作者
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
7#
發(fā)表于 2025-3-22 21:00:40 | 只看該作者
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
8#
發(fā)表于 2025-3-22 23:36:13 | 只看該作者
9#
發(fā)表于 2025-3-23 04:13:13 | 只看該作者
10#
發(fā)表于 2025-3-23 06:22:38 | 只看該作者
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
 關于派博傳思  派博傳思旗下網站  友情鏈接
派博傳思介紹 公司地理位置 論文服務流程 影響因子官網 吾愛論文網 大講堂 北京大學 Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經驗總結 SCIENCEGARD IMPACTFACTOR 派博系數 清華大學 Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網安備110108008328) GMT+8, 2025-10-6 08:50
Copyright © 2001-2015 派博傳思   京公網安備110108008328 版權所有 All rights reserved
快速回復 返回頂部 返回列表
洛川县| 黄冈市| 万载县| 大姚县| 凤城市| 汝城县| 揭东县| 乐昌市| 麻栗坡县| 股票| 丰原市| 荣成市| 邹平县| 望奎县| 清远市| 闸北区| 达尔| 黄冈市| 屏东县| 开封县| 恭城| 商河县| 略阳县| 临泉县| 英山县| 海口市| 云南省| 建平县| 荥经县| 揭西县| 独山县| 岫岩| 西盟| 札达县| 军事| 阿瓦提县| 长子县| 吉安市| 沂源县| 饶阳县| 铁岭市|