Crowd Anomaly Detection Using Standardized Modeled Input.
International Journal of Intelligent Information Systems
Volume 1, Issue 1, December 2012, Pages: 1-6
Received: Dec. 28, 2012; Published: Dec. 30, 2012
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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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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.
Crowd Anomaly Detection, Simulated Crowd, Crowd Scenarios, and Detection Algorithm
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Michael E. Long, Alexander Glade, Kevin J. Bierre, Bartholomew L. Moore, Crowd Anomaly Detection Using Standardized Modeled Input., International Journal of Intelligent Information Systems. Vol. 1, No. 1, 2012, pp. 1-6. doi: 10.11648/j.ijiis.20120101.11
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