article

Surveillance camera scheduling: a virtual vision approach

Authors:

Faisal Z. Qureshi,

Demetri TerzopoulosAuthors Info & Claims

Multimedia Systems, Volume 12, Issue 3

Pages 269 - 283

https://doi.org/10.1007/s00530-006-0059-4

Published: 01 December 2006 Publication History

Abstract

We present a surveillance system, comprising wide field-of-view (FOV) passive cameras and pan/tilt/zoom (PTZ) active cameras, which automatically captures high-resolution videos of pedestrians as they move through a designated area. A wide-FOV static camera can track multiple pedestrians, while any PTZ active camera can capture high-quality videos of one pedestrian at a time. We formulate the multi-camera control strategy as an online scheduling problem and propose a solution that combines the information gathered by the wide-FOV cameras with weighted round-robin scheduling to guide the available PTZ cameras, such that each pedestrian is observed by at least one PTZ camera while in the designated area. A centerpiece of our work is the development and testing of experimental surveillance systems within a visually and behaviorally realistic virtual environment simulator. The simulator is valuable as our research would be more or less infeasible in the real world given the impediments to deploying and experimenting with appropriately complex camera sensor networks in large public spaces. In particular, we demonstrate our surveillance system in a virtual train station environment populated by autonomous, lifelike virtual pedestrians, wherein easily reconfigurable virtual cameras generate synthetic video feeds. The video streams emulate those generated by real surveillance cameras monitoring richly populated public spaces.

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Cited By

Tran MTran M(2023)Anomaly Event Retrieval System from TV News and Surveillance CamerasProceedings of the 12th International Symposium on Information and Communication Technology10.1145/3628797.3628891(953-959)Online publication date: 7-Dec-2023
https://dl.acm.org/doi/10.1145/3628797.3628891
Zhang ZCohen F(2020)3D pedestrian tracking and frontal face image capture based on head point detectionMultimedia Tools and Applications10.1007/s11042-019-08121-y79:1-2(737-764)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1007/s11042-019-08121-y
Bisagno NGarau NMontagner AConci N(2019)Virtual Crowds: An LSTM-Based Framework for Crowd SimulationImage Analysis and Processing – ICIAP 201910.1007/978-3-030-30642-7_11(117-127)Online publication date: 9-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-30642-7_11
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Surveillance camera scheduling: a virtual vision approach
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
2. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
    2. Online algorithms
      1. Online learning algorithms
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning

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Published In

cover image Multimedia Systems

Multimedia Systems Volume 12, Issue 3

December 2006

117 pages

ISSN:0942-4962

Issue’s Table of Contents

Copyright © Copyright © 2006 Springer-Verlag.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 December 2006

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Cited By

Tran MTran M(2023)Anomaly Event Retrieval System from TV News and Surveillance CamerasProceedings of the 12th International Symposium on Information and Communication Technology10.1145/3628797.3628891(953-959)Online publication date: 7-Dec-2023
https://dl.acm.org/doi/10.1145/3628797.3628891
Zhang ZCohen F(2020)3D pedestrian tracking and frontal face image capture based on head point detectionMultimedia Tools and Applications10.1007/s11042-019-08121-y79:1-2(737-764)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1007/s11042-019-08121-y
Bisagno NGarau NMontagner AConci N(2019)Virtual Crowds: An LSTM-Based Framework for Crowd SimulationImage Analysis and Processing – ICIAP 201910.1007/978-3-030-30642-7_11(117-127)Online publication date: 9-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-30642-7_11
Melman SMoses YMedioni GCai Y(2018)The Multi-strand Graph for a PTZ TrackerJournal of Mathematical Imaging and Vision10.1007/s10851-017-0774-960:4(594-608)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s10851-017-0774-9
Murtaza FYousaf MVelastin S(2016)Multi‐view human action recognition using 2D motion templates based on MHIs and their HOG descriptionIET Computer Vision10.1049/iet-cvi.2015.041610:7(758-767)Online publication date: 8-Jun-2016
https://dl.acm.org/doi/10.1049/iet-cvi.2015.0416
Cai YMedioni G(2016)Persistent people tracking and face capture using a PTZ cameraMachine Vision and Applications10.1007/s00138-016-0758-627:3(397-413)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1007/s00138-016-0758-6
Gascueña JFernández-caballero A(2011)Review: on the use of agent technology in intelligent, multisensory and distributed surveillanceThe Knowledge Engineering Review10.1017/S026988891100002626:2(191-208)Online publication date: 12-May-2011
https://dl.acm.org/doi/10.1017/S0269888911000026
Qureshi FWolf MForesti GBischof H(2010)Collaborative sensing via local negotiations in ad hoc networks of smart camerasProceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras10.1145/1865987.1866017(190-197)Online publication date: 31-Aug-2010
https://dl.acm.org/doi/10.1145/1865987.1866017
Terzopoulos DPadgham LParkes D(2008)Autonomous virtual humans and lower animalsProceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 110.5555/1402383.1402390(17-20)Online publication date: 12-May-2008
https://dl.acm.org/doi/10.5555/1402383.1402390
Qureshi FTerzopoulos DAhmad ABragg A(2008)A simulation framework for camera sensor networks researchProceedings of the 11th communications and networking simulation symposium10.1145/1400713.1400720(41-48)Online publication date: 14-Apr-2008
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