Other
motion detection
In this work we consider the problems for video surveillance
applications: (a) abnormal behavior detection and (b)
behavior matching across cameras. We propose busy-idle
rates, meaningful and easy to compute features of foreground
objects, to characterize the behavior profile of a given pixel.
We use these features to model the typical behavior that
is observed in training sequences. Using a small number
of samples for each pixel we generate behavior clusters,
wherein pixels with similar behavior profiles fall into the
same cluster. We then generate probabilistic models corresponding
to behavior clusters, and use these models to
perform abnormal behavior detection.
We next show geometry independence properties of busyidle
rates. Simply stated, a set of objects observed by
multiple cameras, under certain conditions, generate similar
busy-idle stati
c#
检测
运动
No comment