Crowd Anomaly Detection Using Standardized Modeled Input.
Michael E. Long,
Alexander Glade,
Kevin J. Bierre,
Bartholomew L. Moore
Issue:
Volume 1, Issue 1, December 2012
Pages:
1-6
Published:
30 December 2012
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.
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 por...
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