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An integrated multi-modal sensor network for video surveillance

Published: 11 November 2005 Publication History

Abstract

To enhance video surveillance systems, multi-modal sensor integration can be a successful strategy. In this work, a computer vision system able to detect and track people from multiple cameras is integrated with a wireless sensor network mounting PIR (Passive InfraRed) sensors. The two subsystems are briefly described and possible cases in which computer vision algorithms are likely to fail are discussed. Then, simple but reliable outputs from the PIR sensor nodes are exploited to improve the accuracy of the vision system. In particular, two case studies are reported: the first uses the presence detection of PIR sensors to disambiguate between an opened door and a moving person, while the second handles motion direction changes during occlusions. Preliminary results are reported and demonstrate the usefulness of the integration of the two subsystems.

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cover image ACM Conferences
VSSN '05: Proceedings of the third ACM international workshop on Video surveillance & sensor networks
November 2005
168 pages
ISBN:1595932429
DOI:10.1145/1099396
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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Publication History

Published: 11 November 2005

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Author Tags

  1. PIR
  2. multiple cameras
  3. sensor network
  4. tracking
  5. video surveillance

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MM&Sec '05
MM&Sec '05: Multimedia and Security Workshop 2005
November 11, 2005
Hilton, Singapore

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  • (2024)Decentralized multiple hypothesis testing in Cognitive IOT using massive heterogeneous dataCluster Computing10.1007/s10586-024-04324-727:5(6889-6929)Online publication date: 11-Mar-2024
  • (2024)Scanning Systems for Environment Perception in Autonomous NavigationScanning Technologies for Autonomous Systems10.1007/978-3-031-59531-8_2(33-66)Online publication date: 18-Jul-2024
  • (2022)Design of Multimodal Sensor Module for Outdoor Robot Surveillance SystemElectronics10.3390/electronics1114221411:14(2214)Online publication date: 15-Jul-2022
  • (2020)Literature Survey on Multi-Camera System and Its ApplicationIEEE Access10.1109/ACCESS.2020.30245688(172892-172922)Online publication date: 2020
  • (2020)Energy Management Techniques for WSNs (2): Data-Driven ApproachWireless Sensor Networks10.1007/978-3-030-29700-8_5(259-398)Online publication date: 26-Jan-2020
  • (2019)Multilevel Object Tracking in Wireless Multimedia Sensor Networks for Surveillance Applications Using Graph-Based Big DataIEEE Access10.1109/ACCESS.2019.29187657(67818-67832)Online publication date: 2019
  • (2018)Hybrid Sensor Network-Based Indoor Surveillance System for Intrusion DetectionSymmetry10.3390/sym1006018110:6(181)Online publication date: 23-May-2018
  • (2018)Optimal Camera Placement for Multimodal Video SummarizationFuturistic Trends in Network and Communication Technologies10.1007/978-981-13-3804-5_10(123-134)Online publication date: 25-Dec-2018
  • (2016)GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View CasesIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2015.243317226:8(1433-1446)Online publication date: Aug-2016
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