Crowd Anomaly Detection Using Standardized Modeled Input.

Michael E. Long; Alexander Glade; Kevin J. Bierre; Bartholomew L. Moore

doi:doi:10.11648/j.ijiis.20120101.11

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Crowd Anomaly Detection Using Standardized Modeled Input.

Michael E. Long, Alexander Glade, Kevin J. Bierre, Bartholomew L. Moore

Published in International Journal of Intelligent Information Systems (Volume 1, Issue 1)

Received: 28 December 2012 Accepted: Published: 30 December 2012

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Abstract

A variety of techniques exist for tracking and detection of pedestrian traffic.The “proof of concept” or the utility of these methods is often illustrated by analysis of a video or photographs produced by the researcher as part of the development process of the algorithms.Although these images are often based on actual human subjects, they lack portability and ground truth or at best require tedious hand mapping to record ground truth.Hence, each algorithm is developed and tested for a unique situation.Consequently, as an alternative process we propose using gaming techniques to generate pedestrian and crowd like movements that readily produce ground truth referenced via data logs.For this initial study, we have used modifications of the Reynolds flocking model to generate crowd like behavior.Using these algorithms and open-source software platforms, we generated reference crowds and then added individual pedestrian behavior within the simulated crowd.Various detection methods were applied to differentcrowd scenarios to explore and assess the utility of detection methods, illustrate the possibilities of this technique, and demonstrate an initial screening for a detection algorithm.Although not a final proof of a detection process, this method allows facile, rapid, and comparative initial evaluation of the methods under consideration.

Published in	International Journal of Intelligent Information Systems (Volume 1, Issue 1)
DOI	10.11648/j.ijiis.20120101.11
Page(s)	1-6
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Crowd Anomaly Detection, Simulated Crowd, Crowd Scenarios, and Detection Algorithm

References

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APA Style

Michael E. Long, Alexander Glade, Kevin J. Bierre, Bartholomew L. Moore. (2012). Crowd Anomaly Detection Using Standardized Modeled Input.. International Journal of Intelligent Information Systems, 1(1), 1-6. https://doi.org/10.11648/j.ijiis.20120101.11

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ACS Style

Michael E. Long; Alexander Glade; Kevin J. Bierre; Bartholomew L. Moore. Crowd Anomaly Detection Using Standardized Modeled Input.. Int. J. Intell. Inf. Syst. 2012, 1(1), 1-6. doi: 10.11648/j.ijiis.20120101.11

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AMA Style

Michael E. Long, Alexander Glade, Kevin J. Bierre, Bartholomew L. Moore. Crowd Anomaly Detection Using Standardized Modeled Input.. Int J Intell Inf Syst. 2012;1(1):1-6. doi: 10.11648/j.ijiis.20120101.11

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@article{10.11648/j.ijiis.20120101.11,
  author = {Michael E. Long and Alexander Glade and Kevin J. Bierre and Bartholomew L. Moore},
  title = {Crowd Anomaly Detection Using Standardized Modeled Input.},
  journal = {International Journal of Intelligent Information Systems},
  volume = {1},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ijiis.20120101.11},
  url = {https://doi.org/10.11648/j.ijiis.20120101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20120101.11},
  abstract = {A variety of techniques exist for tracking and detection of pedestrian traffic.The “proof of concept” or the utility of these methods is often illustrated by analysis of a video or photographs produced by the researcher as part of the development process of the algorithms.Although these images are often based on actual human subjects, they lack portability and ground truth or at best require tedious hand mapping to record ground truth.Hence, each algorithm is developed and tested for a unique situation.Consequently, as an alternative process we propose using gaming techniques to generate pedestrian and crowd like movements that readily produce ground truth referenced via data logs.For this initial study, we have used modifications of the Reynolds flocking model to generate crowd like behavior.Using these algorithms and open-source software platforms, we generated reference crowds and then added individual pedestrian behavior within the simulated crowd.Various detection methods were applied to differentcrowd scenarios to explore and assess the utility of detection methods, illustrate the possibilities of this technique, and demonstrate an initial screening for a detection algorithm.Although not a final proof of a detection process, this method allows facile, rapid, and comparative initial evaluation of the methods under consideration.},
 year = {2012}
}

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TY  - JOUR
T1  - Crowd Anomaly Detection Using Standardized Modeled Input.
AU  - Michael E. Long
AU  - Alexander Glade
AU  - Kevin J. Bierre
AU  - Bartholomew L. Moore
Y1  - 2012/12/30
PY  - 2012
N1  - https://doi.org/10.11648/j.ijiis.20120101.11
DO  - 10.11648/j.ijiis.20120101.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20120101.11
AB  - A variety of techniques exist for tracking and detection of pedestrian traffic.The “proof of concept” or the utility of these methods is often illustrated by analysis of a video or photographs produced by the researcher as part of the development process of the algorithms.Although these images are often based on actual human subjects, they lack portability and ground truth or at best require tedious hand mapping to record ground truth.Hence, each algorithm is developed and tested for a unique situation.Consequently, as an alternative process we propose using gaming techniques to generate pedestrian and crowd like movements that readily produce ground truth referenced via data logs.For this initial study, we have used modifications of the Reynolds flocking model to generate crowd like behavior.Using these algorithms and open-source software platforms, we generated reference crowds and then added individual pedestrian behavior within the simulated crowd.Various detection methods were applied to differentcrowd scenarios to explore and assess the utility of detection methods, illustrate the possibilities of this technique, and demonstrate an initial screening for a detection algorithm.Although not a final proof of a detection process, this method allows facile, rapid, and comparative initial evaluation of the methods under consideration.
VL  - 1
IS  - 1
ER  -

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Author Information

Michael E. Long

Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA
Alexander Glade

B. Thomas Golisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY, USA
Kevin J. Bierre

B. Thomas Golisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY, USA
Bartholomew L. Moore

Second Avenue Software, Inc., Pittsford, NY, USA

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Plain Text BibTeX RIS

APA Style

Michael E. Long, Alexander Glade, Kevin J. Bierre, Bartholomew L. Moore. (2012). Crowd Anomaly Detection Using Standardized Modeled Input.. International Journal of Intelligent Information Systems, 1(1), 1-6. https://doi.org/10.11648/j.ijiis.20120101.11

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ACS Style

Michael E. Long; Alexander Glade; Kevin J. Bierre; Bartholomew L. Moore. Crowd Anomaly Detection Using Standardized Modeled Input.. Int. J. Intell. Inf. Syst. 2012, 1(1), 1-6. doi: 10.11648/j.ijiis.20120101.11

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AMA Style

Michael E. Long, Alexander Glade, Kevin J. Bierre, Bartholomew L. Moore. Crowd Anomaly Detection Using Standardized Modeled Input.. Int J Intell Inf Syst. 2012;1(1):1-6. doi: 10.11648/j.ijiis.20120101.11

Copy | Download

@article{10.11648/j.ijiis.20120101.11,
  author = {Michael E. Long and Alexander Glade and Kevin J. Bierre and Bartholomew L. Moore},
  title = {Crowd Anomaly Detection Using Standardized Modeled Input.},
  journal = {International Journal of Intelligent Information Systems},
  volume = {1},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ijiis.20120101.11},
  url = {https://doi.org/10.11648/j.ijiis.20120101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20120101.11},
  abstract = {A variety of techniques exist for tracking and detection of pedestrian traffic.The “proof of concept” or the utility of these methods is often illustrated by analysis of a video or photographs produced by the researcher as part of the development process of the algorithms.Although these images are often based on actual human subjects, they lack portability and ground truth or at best require tedious hand mapping to record ground truth.Hence, each algorithm is developed and tested for a unique situation.Consequently, as an alternative process we propose using gaming techniques to generate pedestrian and crowd like movements that readily produce ground truth referenced via data logs.For this initial study, we have used modifications of the Reynolds flocking model to generate crowd like behavior.Using these algorithms and open-source software platforms, we generated reference crowds and then added individual pedestrian behavior within the simulated crowd.Various detection methods were applied to differentcrowd scenarios to explore and assess the utility of detection methods, illustrate the possibilities of this technique, and demonstrate an initial screening for a detection algorithm.Although not a final proof of a detection process, this method allows facile, rapid, and comparative initial evaluation of the methods under consideration.},
 year = {2012}
}

Copy | Download

TY  - JOUR
T1  - Crowd Anomaly Detection Using Standardized Modeled Input.
AU  - Michael E. Long
AU  - Alexander Glade
AU  - Kevin J. Bierre
AU  - Bartholomew L. Moore
Y1  - 2012/12/30
PY  - 2012
N1  - https://doi.org/10.11648/j.ijiis.20120101.11
DO  - 10.11648/j.ijiis.20120101.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20120101.11
AB  - A variety of techniques exist for tracking and detection of pedestrian traffic.The “proof of concept” or the utility of these methods is often illustrated by analysis of a video or photographs produced by the researcher as part of the development process of the algorithms.Although these images are often based on actual human subjects, they lack portability and ground truth or at best require tedious hand mapping to record ground truth.Hence, each algorithm is developed and tested for a unique situation.Consequently, as an alternative process we propose using gaming techniques to generate pedestrian and crowd like movements that readily produce ground truth referenced via data logs.For this initial study, we have used modifications of the Reynolds flocking model to generate crowd like behavior.Using these algorithms and open-source software platforms, we generated reference crowds and then added individual pedestrian behavior within the simulated crowd.Various detection methods were applied to differentcrowd scenarios to explore and assess the utility of detection methods, illustrate the possibilities of this technique, and demonstrate an initial screening for a detection algorithm.Although not a final proof of a detection process, this method allows facile, rapid, and comparative initial evaluation of the methods under consideration.
VL  - 1
IS  - 1
ER  -

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