One of the key components of tele-presence systems is automatic awareness of the remote environment. This very same capability of automatic situation awareness is currently being developed and deployed in the context of the next generation smart surveillance systems. Smart surveillance systems use a number of automatic video analysis techniques like object detection, tracking and classification in conjunction with database and web application servers to provide users with the capability of distributed smart surveillance. The IBM smart surveillance system is one of the few advanced surveillance systems which provides not only the capability to automatically monitor a scene but also the capability to manage the surveillance data, perform event based retrieval, receive real time event alerts thru standard web infrastructure and extract long term statistical patterns of activity.

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Purkayastha DBasak SMondal RGharami SRoy PDey PGhosh T(2024)Enhancing Security and Efficiency with Smart Surveillance Using Machine Learning2024 Parul International Conference on Engineering and Technology (PICET)10.1109/PICET60765.2024.10716062(1-7)Online publication date: 3-May-2024
https://doi.org/10.1109/PICET60765.2024.10716062
Niu TLiu JZhang ZGuo Y(2024)Detecting Potential Risks of Emergency Events in Automated Warehousing Systems2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)10.1109/ICECAI62591.2024.10675274(489-493)Online publication date: 31-May-2024
https://doi.org/10.1109/ICECAI62591.2024.10675274
Borah JSarma KGohain P(2019)All Pervasive Surveillance Techniques and AI-Based ApplicationsSmart Devices, Applications, and Protocols for the IoT10.4018/978-1-5225-7811-6.ch004(54-82)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-7811-6.ch004
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Index Terms

S3-R1: the IBM smart surveillance system-release 1
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
        Tracking
      2. Computer vision tasks
        Scene understanding
2. Information systems
  1. Information retrieval
    1. Document representation
    2. Search engine architectures and scalability
      1. Search engine indexing

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Published In

ETP '04: Proceedings of the 2004 ACM SIGMM workshop on Effective telepresence

October 2004

82 pages

ISBN:1581139330

DOI:10.1145/1026776

General Chairs:
Gopal Pingali
IBM T.J. Watson Research Center
,
Frank Nack
CWI, The Netherlands
,
Hari Sundaram
Arizona State University, Tempe, AZ

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 15 October 2004

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Purkayastha DBasak SMondal RGharami SRoy PDey PGhosh T(2024)Enhancing Security and Efficiency with Smart Surveillance Using Machine Learning2024 Parul International Conference on Engineering and Technology (PICET)10.1109/PICET60765.2024.10716062(1-7)Online publication date: 3-May-2024
https://doi.org/10.1109/PICET60765.2024.10716062
Niu TLiu JZhang ZGuo Y(2024)Detecting Potential Risks of Emergency Events in Automated Warehousing Systems2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)10.1109/ICECAI62591.2024.10675274(489-493)Online publication date: 31-May-2024
https://doi.org/10.1109/ICECAI62591.2024.10675274
Borah JSarma KGohain P(2019)All Pervasive Surveillance Techniques and AI-Based ApplicationsSmart Devices, Applications, and Protocols for the IoT10.4018/978-1-5225-7811-6.ch004(54-82)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-7811-6.ch004
Xie JStefanov ALiu C(2019)Physical and Cybersecurity in a Smart Grid EnvironmentAdvances in Energy Systems10.1002/9781119508311.ch5(85-109)Online publication date: 8-Feb-2019
https://doi.org/10.1002/9781119508311.ch5
Lee YKim JKyung C(2018)Energy-Aware Video Encoding for Image Quality Improvement in Battery-Operated Surveillance CameraIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2010.210205520:2(310-318)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1109/TVLSI.2010.2102055
Kumar HBhattacharya SThomas SGupta SVenkatesh K(2017)Design of smart video surveillance system for indoor and outdoor scenes2017 22nd International Conference on Digital Signal Processing (DSP)10.1109/ICDSP.2017.8096120(1-5)Online publication date: Aug-2017
https://doi.org/10.1109/ICDSP.2017.8096120
Tiwari SChowdhury MGoswami USadhu P(2016)Low power customized new trend remote surveillance system over 3G mobile network2016 3rd International Conference on Recent Advances in Information Technology (RAIT)10.1109/RAIT.2016.7507872(41-45)Online publication date: Mar-2016
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Xie JStefanov ALiu C(2016)Physical and cyber security in a smart grid environmentWIREs Energy and Environment10.1002/wene.2025:5(519-542)Online publication date: 4-Mar-2016
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Xie JLiu CSforna MBilek MHamza R(2015)On-line physical security monitoring of power substationsInternational Transactions on Electrical Energy Systems10.1002/etep.212226:6(1148-1170)Online publication date: 9-Sep-2015
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Azhar HAmer A(2012)Classification of surveillance video objects using chaotic seriesIET Image Processing10.1049/iet-ipr.2011.02696:7(919)Online publication date: 2012
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IBM smart surveillance system (S3): event based video surveillance system with an open and extensible framework

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