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Anomaly Detection in Extremely Crowded Scenes Using Spatio-Temporal Motion Pattern Models
Louis Kratz and Ko Nishino |
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Abstract
Extremely crowded scenes present unique challenges to
video analysis that cannot be addressed with conventional
approaches. We present a novel statistical framework for
modeling the local spatio-temporal motion pattern behavior
of extremely crowded scenes. Our key insight is to exploit
the dense activity of the crowded scene by modeling
the rich motion patterns in local areas, effectively capturing
the underlying intrinsic structure they form in the video. In
other words, we model the motion variation of local spacetime
volumes and their spatial-temporal statistical behaviors
to characterize the overall behavior of the scene. We
demonstrate that by capturing the steady-state motion behavior
with these spatio-temporal motion pattern models,
we can naturally detect unusual activity as statistical deviations.
Our experiments show that local spatio-temporal
motion pattern modeling offers promising results in realworld
scenes with complex activities that are hard for even
human observers to analyze.
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