research-article

Fusing multiple video sensors for surveillance

Authors:

Ingrid Visentini,

Gian Luca ForestiAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 8, Issue 1

Article No.: 7, Pages 1 - 18

https://doi.org/10.1145/2071396.2071403

Published: 03 February 2012 Publication History

Abstract

Real-time detection, tracking, recognition, and activity understanding of moving objects from multiple sensors represent fundamental issues to be solved in order to develop surveillance systems that are able to autonomously monitor wide and complex environments. The algorithms that are needed span therefore from image processing to event detection and behaviour understanding, and each of them requires dedicated study and research. In this context, sensor fusion plays a pivotal role in managing the information and improving system performance. Here we present a novel fusion framework for combining the data coming from multiple and possibly heterogeneous sensors observing a surveillance area.

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Cited By

Kizilkaya BEver EYatbaz HYazici A(2022)An Effective Forest Fire Detection Framework Using Heterogeneous Wireless Multimedia Sensor NetworksACM Transactions on Multimedia Computing, Communications, and Applications10.1145/347303718:2(1-21)Online publication date: 16-Feb-2022
https://dl.acm.org/doi/10.1145/3473037
Kong LDai R(2018)Efficient Video Encoding for Automatic Video Analysis in Distributed Wireless Surveillance SystemsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/322603614:3(1-24)Online publication date: 24-Jul-2018
https://dl.acm.org/doi/10.1145/3226036
Li QDu JZhu SXu L(2017)Adaptive multiple video sensors fusion based on decentralized Kalman filter and sensor confidence基于分散式卡尔曼滤波和传感器可信度的自适应视频传感器融合Science China Information Sciences10.1007/s11432-015-5450-360:6Online publication date: 8-Feb-2017
https://doi.org/10.1007/s11432-015-5450-3
Show More Cited By

Index Terms

Fusing multiple video sensors for surveillance
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding
        Vision for robotics

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 8, Issue 1

January 2012

149 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/2071396

Issue’s Table of Contents

Copyright © 2012 ACM.

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 February 2012

Accepted: 01 October 2010

Revised: 01 July 2010

Received: 01 January 2010

Published in TOMM Volume 8, Issue 1

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Author Tag

Multicamera video surveillance

Qualifiers

Research-article
Research
Refereed

Funding Sources

European Defence Agency in DAFNE (Distributed and Adaptive multisensor FusioN Engine)

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Cited By

Kizilkaya BEver EYatbaz HYazici A(2022)An Effective Forest Fire Detection Framework Using Heterogeneous Wireless Multimedia Sensor NetworksACM Transactions on Multimedia Computing, Communications, and Applications10.1145/347303718:2(1-21)Online publication date: 16-Feb-2022
https://dl.acm.org/doi/10.1145/3473037
Kong LDai R(2018)Efficient Video Encoding for Automatic Video Analysis in Distributed Wireless Surveillance SystemsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/322603614:3(1-24)Online publication date: 24-Jul-2018
https://dl.acm.org/doi/10.1145/3226036
Li QDu JZhu SXu L(2017)Adaptive multiple video sensors fusion based on decentralized Kalman filter and sensor confidence基于分散式卡尔曼滤波和传感器可信度的自适应视频传感器融合Science China Information Sciences10.1007/s11432-015-5450-360:6Online publication date: 8-Feb-2017
https://doi.org/10.1007/s11432-015-5450-3
Snidaro LVisentini I(2016)Context for Dynamic and Multi-level FusionContext-Enhanced Information Fusion10.1007/978-3-319-28971-7_16(431-451)Online publication date: 26-May-2016
https://doi.org/10.1007/978-3-319-28971-7_16
Chen BHuang S(2015)An Advanced Visibility Restoration Algorithm for Single Hazy ImagesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/272694711:4(1-21)Online publication date: 2-Jun-2015
https://dl.acm.org/doi/10.1145/2726947
Laouira MAbdelli AMokdad LOthman J(2015)An efficient transmitting strategy for image fusion in WMSN2015 IEEE International Conference on Communications (ICC)10.1109/ICC.2015.7249170(5325-5330)Online publication date: Jun-2015
https://doi.org/10.1109/ICC.2015.7249170
Zhang QWang YLevine MYuan XWang L(2015)Multisensor video fusion based on higher order singular value decompositionInformation Fusion10.1016/j.inffus.2014.09.00824:C(54-71)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.1016/j.inffus.2014.09.008
Kumar PJain MPavan ABhat MManjunath C(2013)Intelligent Collaborative Surveillance System2013 International Conference on Advances in Technology and Engineering (ICATE)10.1109/ICAdTE.2013.6524717(1-6)Online publication date: Jan-2013
https://doi.org/10.1109/ICAdTE.2013.6524717

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