Video Analytics for Business Intelligence

  • Caifeng Shan
  • Fatih Porikli
  • Tao Xiang
  • Shaogang Gong

Part of the Studies in Computational Intelligence book series (SCI, volume 409)

Table of contents

  1. Front Matter
    Pages 1-10
  2. Computational Vision

    1. Front Matter
      Pages 1-1
    2. Fatih Porikli, Alper Yilmaz
      Pages 3-41
    3. Cristina Picus, Roman Pflugfelder, Branislav Micusik
      Pages 43-67
  3. Demographics

    1. Front Matter
      Pages 99-99
    2. Amit Khemlani, Kester Duncan, Sudeep Sarkar
      Pages 133-159
    3. David Ryan, Simon Denman, Sridha Sridharan, Clinton Fookes
      Pages 161-198
    4. Simon Denman, Alina Bialkowski, Clinton Fookes, Sridha Sridharan
      Pages 199-238
  4. Behaviour Analysis

    1. Front Matter
      Pages 239-239
    2. Eran Swears, Matthew Turek, Roderic Collins, A. G. Amitha Perera, Anthony Hoogs
      Pages 241-269
    3. Loris Bazzani, Marco Cristani, Giulia Paggetti, Diego Tosato, Gloria Menegaz, Vittorio Murino
      Pages 271-305
  5. Systems

    1. Front Matter
      Pages 307-307
    2. Asaad Hakeem, Himaanshu Gupta, Atul Kanaujia, Tae Eun Choe, Kiran Gunda, Andrew Scanlon et al.
      Pages 309-354
    3. Vasu Parameswaran, Vinay Shet, Visvanathan Ramesh
      Pages 355-373
  6. Back Matter
    Pages 0--1

About this book

Introduction

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 oriented commercial applications. This book presents the latest developments on video analytics for business intelligence applications. It provides both academic and commercial practitioners an understanding of the state-of-the-art and a resource for potential applications and successful practice.

Keywords

Business Intelligence Computational Intelligence Computational Vision Video Analytics

Editors and affiliations

  • Caifeng Shan
    • 1
  • Fatih Porikli
    • 2
  • Tao Xiang
    • 3
  • Shaogang Gong
    • 4
  1. 1.Philips ResearchEindhovenNetherlands
  2. 2.Mitsubishi Electric Research LaboratorieCambridgeUSA
  3. 3.Dept. Computer ScienceQueen Mary University of LondonLondonUnited Kingdom
  4. 4.Dept. Computer ScienceQueen Mary University of LondonLondonUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-28598-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-28597-4
  • Online ISBN 978-3-642-28598-1
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book
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