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| Unsupervised Detection of Abnormal Behavior for Intelligent Surveillance |
| Changqi Zhang, Yepeng Guan |
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Abstract A novel method based on dynamic particle flow has been developed to
detect abnormal behavior automatically. The proposed approach starts
by taking motion crowds as an aperiodic dynamical flow field to
solve abnormal behavior detection problem. The motion in the scene
is described by the particle trajectories computed by time
integration of the dynamical flow field. Abnormal behavior is
detected automatically based on some high-level features extracted
from the developed particle flow without any hypothesis for the
scene conditions in advance. The proposal does not require any
object detection, tracking, or training in detecting abnormal
behaviors. Comparative studies with some investigated methods have
indicated the superior performance of the proposal in detecting
abnormal behaviors from cluttered contents with dynamic scene
variation and crowded environments.
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| DOI:10.12733/jics20104372 |
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